数据理论_数据库_计算机类_最新资料_得益网

数据库系统原理(张敬宇)

2008-11-12 14:50:11

第1章数据库系统基本概念
第2章数据模型与概念模型
第3章数据库设计
第4章关系数据库
第5章关系数据库标准语言——SQL
第7章关系数据库理论
第8章数据库保护

Moving Objects Databases

2008-10-01 14:55:00

关于移动对象数据库的经典教材

Chapter 1 Introduction 1
1.1 Database Management Systems 1
1.2 Spatial Databases 3
1.2.1 Modeling Spatial Concepts 4
1.2.2 Extending Data Model and Query Language 6
1.2.3 Implementation Strategy 9
1.3 Temporal Databases 9
1.3.1 Managing Time in Standard Databases 9
1.3.2 The Time Domain 10
1.3.3 Time Dimensions 11
1.3.4 Extending the Data Model 12
1.3.5 Extending the Query Language: TSQL2 19
1.4 Moving Objects 21
1.4.1 The Location Management Perspective 21
1.4.2 The Spatio-Temporal Data Perspective 22
1.4.3 Moving Objects and Questions about Them 23
1.4.4 A Classification of Spatio-Temporal Data 23
1.4.5 Temporal Databases with Spatial Data Types 26
1.4.6 Spatio-Temporal Data Types 27
1.5 Further Exercises 29
1.6 Bibliographic Notes 30

Chapter 2 Spatio-Temporal Databases in the Past 33
2.1 Spatio-Bitemporal Objects 33
2.1.1 An Application Scenario 33
2.1.2 Bitemporal Elements 35
2.1.3 Spatial Objects Modeled as Simplicial Complexes 36
2.1.4 Spatio-Bitemporal Objects 39
2.1.5 Spatio-Bitemporal Operations 41
2.1.6 Querying 46
2.2 An Event-Based Approach 48
2.2.1 The Model 48
2.2.2 Query Processing Algorithms 51
2.3 Further Exercises 53
2.4 Bibliographic Notes 54

Chapter 3 Modeling and Querying Current Movement 57
3.1 Location Management 57
3.2 MOST—A Data Model for Current and Future Movement 59
3.2.1 Basic Assumptions 59
3.2.2 Dynamic Attributes 60
3.2.3 Representing Object Positions 61
3.2.4 Database Histories 61
3.2.5 Three Types of Queries 62
3.3 FTL—A Query Language Based on Future Temporal Logic 64
3.3.1 Some Example Queries 64
3.3.2 Syntax 66
3.3.3 Semantics 68
3.3.4 Evaluating FTL Queries 71
3.4 Location Updates—Balancing Update Cost and Imprecision 77
3.4.1 Background 77
3.4.2 The Information Cost of a Trip 78
3.4.3 Cost-Based Optimization for Dead-Reckoning Policies 80
3.4.4 Dead-Reckoning Location Update Policies 82
3.5 The Uncertainty of the Trajectory of a Moving Object 84
3.5.1 A Model of a Trajectory 85
3.5.2 Uncertainty Concepts for Trajectories 86
3.5.3 Querying Moving Objects with Uncertainty 88
3.5.4 Algorithms for Spatio-Temporal Operations and Predicates 91
3.6 Further Exercises 95
3.7 Bibliographic Notes 97

Chapter 4 Modeling and Querying History of Movement 99
4.1 An Approach Based on Abstract Data Types 99
4.1.1 Types and Operations 99
4.1.2 Abstract versus Discrete Models 102
4.1.3 Language Embedding of Abstract Data Types 104
4.2 An Abstract Model 105
4.2.1 Data Types 106
4.2.2 Formal Definition of Data Types 108
4.2.3 Overview of Operations 113
4.2.4 Operations on Nontemporal Types 114
4.2.5 Operations on Temporal Types 121
4.2.6 Operations on Sets of Objects 133
4.3 A Discrete Model 136
4.3.1 Overview 136
4.3.2 Nontemporal Types 139
4.3.3 Temporal Types 143
4.4 Spatio-Temporal Predicates and Developments 150
4.4.1 Motivation 151
4.4.2 Topological Predicates for Spatial Objects 152
4.4.3 The Problem of Temporally Lifting Topological Predicates 155
4.4.4 Temporal Aggregation 156
4.4.5 Basic Spatio-Temporal Predicates 157
4.4.6 Developments: Sequences of Spatio-Temporal Predicates 159
4.4.7 A Concise Syntax for Developments 162
4.4.8 An Algebra of Spatio-Temporal Predicates 165
4.4.9 Examples 172
4.4.10 A Canonical Collection of Spatio-Temporal Predicates 174
4.4.11 Querying Developments in STQL 177
4.5 Further Exercises 181
4.6 Bibliographic Notes 184

Chapter 5 Data Structures and Algorithms for Moving Objects Types 187
5.1 Data Structures 187
5.1.1 General Requirements and Strategy 187
5.1.2 Nontemporal Data Types 188
5.1.3 Temporal Data Types 190
5.2 Algorithms for Operations on Temporal Data Types 192
5.2.1 Common Considerations 192
5.2.2 Projection to Domain and Range 195
5.2.3 Interaction with Domain/Range 197
5.2.4 Rate of Change 202
5.3 Algorithms for Lifted Operations 204
5.3.1 Predicates 205
5.3.2 Set Operations 208
5.3.3 Aggregation 210
5.3.4 Numeric Properties 211
5.3.5 Distance and Direction 212
5.3.6 Boolean Operations 215
5.4 Further Exercises 215
5.5 Bibliographic Notes 216

Chapter 6 The Constraint Database Approach 217
6.1 An Abstract Model: Infinite Relations 218
6.1.1 Flat Relations 218
6.1.2 Nested Relations 223
6.1.3 Conclusion 225
6.2 A Discrete Model: Constraint Relations 225
6.2.1 Spatial Modeling with Constraints 225
6.2.2 The Linear Constraint Data Model 229
6.2.3 Relational Algebra for Constraint Relations 230
6.3 Implementation of the Constraint Model 239
6.3.1 Representation of Relations 239
6.3.2 Representation of Symbolic Relations (Constraint Formulas) 239
6.3.3 Data Loading and Conversion 240
6.3.4 Normalization of Symbolic Tuples 250
6.3.5 Implementation of Algebra Operations 254
6.4 Further Exercises 257
6.5 Bibliographic Notes 259

Chapter 7 Spatio-Temporal Indexing 261
7.1 Geometric Preliminaries 262
7.1.1 Indexing Multidimensional Space with the R-tree Family 262
7.1.2 Duality 266
7.1.3 External Partition Tree 267
7.1.4 Catalog Structure 270
7.1.5 External Priority Search Tree 271
7.1.6 External Range Tree 272
7.2 Requirements for Indexing Moving Objects 274
7.2.1 Specifics of Spatio-Temporal Index Structures 274
7.2.2 Specification Criteria for Spatio-Temporal Index Structures 277
7.2.3 A Survey of STAMs in the Past 279
7.3 Indexing Current and Near-Future Movement 281
7.3.1 General Strategies 282
7.3.2 The TPR-tree 283
7.3.3 The Dual Data Transformation Approach 292
7.3.4 Time-Oblivious Indexing with Multilevel Partition Trees 299
7.3.5 Kinetic B-trees 301
7.3.6 Kinetic External Range Trees 301
7.3.7 Time-Responsive Indexing with Multiversion Kinetic B-trees 303
7.3.8 Time-Responsive Indexing with Multiversion External Kinetic Range Trees 304
7.4 Indexing Trajectories (History of Movement) 306
7.4.1 The STR-tree 307
7.4.2 The TB-tree 310
7.4.3 Query Processing 312
7.5 Further Exercises 316
7.6 Bibliographic Notes 319

Chapter 8 Outlook 321
8.1 Data Capture 321
8.2 Generating Test Data 322
8.3 Movement in Networks 323
8.4 Query Processing for Continuous/Location-Based Queries 325
8.5 Aggregation and Selectivity Estimation 326
Solutions to Exercises in the Text 329
Bibliography 357
Citation Index 371
Index 375
About the Authors 389

先进数据挖掘技术（Advanced Data Mining Techniques2008）

2008-07-06 13:51:44

1 Introduction...............................................................................................3
What is Data Mining?..........................................................................5
What is Needed to Do Data Mining.....................................................5
Business Data Mining..........................................................................7
Data Mining Tools ...............................................................................8
Summary..............................................................................................8
2 Data Mining Process.................................................................................9
CRISP-DM ..........................................................................................9
Business Understanding.............................................................11
Data Understanding ...................................................................11
Data Preparation ........................................................................12
Modeling ...................................................................................15
Evaluation..................................................................................18
Deployment................................................................................18
SEMMA.............................................................................................19
Steps in SEMMA Process..........................................................20
Example Data Mining Process Application.......................................22
Comparison of CRISP & SEMMA....................................................27
Handling Data....................................................................................28
Summary............................................................................................34
3 Memory-Based Reasoning Methods.......................................................39
Matching ............................................................................................40
Weighted Matching....................................................................43
Distance Minimization.......................................................................44
Software.............................................................................................50
Summary............................................................................................50
Appendix: Job Application Data Set..................................................51
4 Association Rules in Knowledge Discovery........................................... 53
Market-Basket Analysis.....................................................................55
Market Basket Analysis Benefits...............................................56
Demonstration on Small Set of Data ......................................... 57
Real Market Basket Data ................................................................... 59
The Counting Method Without Software ..................................62
Conclusions........................................................................................68
5 Fuzzy Sets in Data Mining...................................................................... 69
Fuzzy Sets and Decision Trees .......................................................... 71
Fuzzy Sets and Ordinal Classification ............................................... 75
Fuzzy Association Rules....................................................................79
Demonstration Model ................................................................80
Computational Results...............................................................84
Testing .......................................................................................84
Inferences...................................................................................85
Conclusions........................................................................................86
6 Rough Sets .............................................................................................. 87
A Brief Theory of Rough Sets ........................................................... 88
Information System....................................................................88
Decision Table ...........................................................................89
Some Exemplary Applications of Rough Sets................................... 91
Rough Sets Software Tools................................................................93
The Process of Conducting Rough Sets Analysis.............................. 93
1 Data Pre-Processing................................................................94
2 Data Partitioning .....................................................................95
3 Discretization ..........................................................................95
4 Reduct Generation ..................................................................97
5 Rule Generation and Rule Filtering ........................................ 99
6 Apply the Discretization Cuts to Test Dataset...................... 100
7 Score the Test Dataset on Generated Rule set (and
measuring the prediction accuracy) ...................................... 100
8 Deploying the Rules in a Production System ....................... 102
A Representative Example...............................................................103
Conclusion .......................................................................................109
7 Support Vector Machines ..................................................................... 111
Formal Explanation of SVM............................................................112
Primal Form.............................................................................114
Dual Form................................................................................114
Soft Margin..............................................................................114
Non-linear Classification .................................................................115
Regression................................................................................116
Implementation ........................................................................116
Kernel Trick.............................................................................117
Use of SVM – A Process-Based Approach .....................................118
Support Vector Machines versus Artificial Neural Networks .........121
Disadvantages of Support Vector Machines....................................122
8 Genetic Algorithm Support to Data Mining .........................................125
Demonstration of Genetic Algorithm ..............................................126
Application of Genetic Algorithms in Data Mining ........................131
Summary..........................................................................................132
Appendix: Loan Application Data Set.............................................133
9 Performance Evaluation for Predictive Modeling ................................137
Performance Metrics for Predictive Modeling ................................137
Estimation Methodology for Classification Models ........................140
Simple Split (Holdout).....................................................................140
The k-Fold Cross Validation............................................................141
Bootstrapping and Jackknifing ........................................................143
Area Under the ROC Curve.............................................................144
Summary..........................................................................................147
10 Applications of Methods ....................................................................151
Memory-Based Application.............................................................151
Association Rule Application ..........................................................153
Fuzzy Data Mining ..........................................................................155
Rough Set Models............................................................................155
Support Vector Machine Application ..............................................157
Genetic Algorithm Applications......................................................158
Japanese Credit Screening .......................................................158
Product Quality Testing Design...............................................159
Customer Targeting .................................................................159
Medical Analysis .....................................................................160
Predicting the Financial Success of Hollywood Movies ................. 162
Problem and Data Description.................................................163
Comparative Analysis of the Data Mining Methods ............... 165
Conclusions......................................................................................167
Bibliography ............................................................................................ 169
Index ........................................................................................................ 177

数据挖掘论文收藏(10篇)

2008-06-18 20:37:00

压缩包中10篇有关数据挖掘的论文，题目如下所示：
1、多媒体数据集中的数据挖掘：系统框架和方法.pdf
2、基于数据挖掘的模块评估法.pdf
3、基于数据挖掘技术的高校管理决策支持系统.pdf
4、空间数据挖掘技术.pdf
5、空间数据挖掘与发展趋势研究.pdf
6、数据挖掘及其应用研究回顾.pdf
7、数据挖掘技术及其应用简介.pdf
8、数据挖掘技术在商业银行中的应用研究.pdf
9、数据挖掘在商务中的应用.pdf
10、遗传算法在数据挖掘中的应用.pdf
建议使用最新版的Adobe Reader 8打开该PDF文档。

数据挖掘：概念与技术（英文影印版·第2版）

2008-06-17 23:05:43

Chapter 1 Introduction 1
1.1 What Motivated Data Mining? Why Is It Important? 1
1.2 So, What Is Data Mining? 5
1.3 Data Mining桹n What Kind of Data? 9
1.3.1 Relational Databases 10
1.3.2 Data Warehouses 12
1.3.3 Transactional Databases 14
1.3.4 Advanced Data and Information Systems and Advanced
Applications 15
1.4 Data Mining Functionalities梂hat Kinds of Patterns Can Be
Mined? 21
1.4.1 Concept/Class Description: Characterization and
Discrimination 21
1.4.2 Mining Frequent Patterns, Associations, and Correlations 23
1.4.3 Classification and Prediction 24
1.4.4 Cluster Analysis 25
1.4.5 Outlier Analysis 26
1.4.6 Evolution Analysis 27
1.5 Are All of the Patterns Interesting? 27
1.6 Classification of Data Mining Systems 29
1.7 Data Mining Task Primitives 31
1.8 Integration of a Data Mining System with
a Database or DataWarehouse System 34
1.9 Major Issues in Data Mining 36
ix
x Contents
1.10 Summary 39
Exercises 40
Bibliographic Notes 42
Chapter 2 Data Preprocessing 47
2.1 Why Preprocess the Data? 48
2.2 Descriptive Data Summarization 51
2.2.1 Measuring the Central Tendency 51
2.2.2 Measuring the Dispersion of Data 53
2.2.3 Graphic Displays of Basic Descriptive Data Summaries 56
2.3 Data Cleaning 61
2.3.1 Missing Values 61
2.3.2 Noisy Data 62
2.3.3 Data Cleaning as a Process 65
2.4 Data Integration and Transformation 67
2.4.1 Data Integration 67
2.4.2 Data Transformation 70
2.5 Data Reduction 72
2.5.1 Data Cube Aggregation 73
2.5.2 Attribute Subset Selection 75
2.5.3 Dimensionality Reduction 77
2.5.4 Numerosity Reduction 80
2.6 Data Discretization and Concept Hierarchy Generation 86
2.6.1 Discretization and Concept Hierarchy Generation for
Numerical Data 88
2.6.2 Concept Hierarchy Generation for Categorical Data 94
2.7 Summary 97
Exercises 97
Bibliographic Notes 101
Chapter 3 DataWarehouse and OLAP Technology: An Overview 105
3.1 What Is a DataWarehouse? 105
3.1.1 Differences between Operational Database Systems
and Data Warehouses 108
3.1.2 But, Why Have a Separate Data Warehouse? 109
3.2 A Multidimensional Data Model 110
3.2.1 From Tables and Spreadsheets to Data Cubes 110
3.2.2 Stars, Snowflakes, and Fact Constellations:
Schemas for Multidimensional Databases 114
3.2.3 Examples for Defining Star, Snowflake,
and Fact Constellation Schemas 117
Contents xi
3.2.4 Measures: Their Categorization and Computation 119
3.2.5 Concept Hierarchies 121
3.2.6 OLAP Operations in the Multidimensional Data Model 123
3.2.7 A Starnet Query Model for Querying
Multidimensional Databases 126
3.3 DataWarehouse Architecture 127
3.3.1 Steps for the Design and Construction of Data Warehouses 128
3.3.2 A Three-Tier Data Warehouse Architecture 130
3.3.3 Data Warehouse Back-End Tools and Utilities 134
3.3.4 Metadata Repository 134
3.3.5 Types of OLAP Servers: ROLAP versus MOLAP
versus HOLAP 135
3.4 DataWarehouse Implementation 137
3.4.1 Efficient Computation of Data Cubes 137
3.4.2 Indexing OLAP Data 141
3.4.3 Efficient Processing of OLAP Queries 144
3.5 From DataWarehousing to Data Mining 146
3.5.1 Data Warehouse Usage 146
3.5.2 From On-Line Analytical Processing
to On-Line Analytical Mining 148
3.6 Summary 150
Exercises 152
Bibliographic Notes 154
Chapter 4 Data Cube Computation and Data Generalization 157
4.1 Efficient Methods for Data Cube Computation 157
4.1.1 A Road Map for the Materialization of Different Kinds
of Cubes 158
4.1.2 Multiway Array Aggregation for Full Cube Computation 164
4.1.3 BUC: Computing Iceberg Cubes from the Apex Cuboid
Downward 168
4.1.4 Star-cubing: Computing Iceberg Cubes Using
a Dynamic Star-tree Structure 173
4.1.5 Precomputing Shell Fragments for Fast High-Dimensional
OLAP 178
4.1.6 Computing Cubes with Complex Iceberg Conditions 187
4.2 Further Development of Data Cube and OLAP
Technology 189
4.2.1 Discovery-Driven Exploration of Data Cubes 189
4.2.2 Complex Aggregation at Multiple Granularity:
Multifeature Cubes 192
4.2.3 Constrained Gradient Analysis in Data Cubes 195
xii Contents
4.3 Attribute-Oriented Induction桝n Alternative
Method for Data Generalization and Concept Description 198
4.3.1 Attribute-Oriented Induction for Data Characterization 199
4.3.2 Efficient Implementation of Attribute-Oriented Induction 205
4.3.3 Presentation of the Derived Generalization 206
4.3.4 Mining Class Comparisons: Discriminating between
Different Classes 210
4.3.5 Class Description: Presentation of Both Characterization
and Comparison 215
4.4 Summary 218
Exercises 219
Bibliographic Notes 223
Chapter 5 Mining Frequent Patterns, Associations, and Correlations 227
5.1 Basic Concepts and a Road Map 227
5.1.1 Market Basket Analysis: A Motivating Example 228
5.1.2 Frequent Itemsets, Closed Itemsets, and Association Rules 230
5.1.3 Frequent Pattern Mining: A Road Map 232
5.2 Efficient and Scalable Frequent Itemset Mining Methods 234
5.2.1 The Apriori Algorithm: Finding Frequent Itemsets Using
Candidate Generation 234
5.2.2 Generating Association Rules from Frequent Itemsets 239
5.2.3 Improving the Efficiency of Apriori 240
5.2.4 Mining Frequent Itemsets without Candidate Generation 242
5.2.5 Mining Frequent Itemsets Using Vertical Data Format 245
5.2.6 Mining Closed Frequent Itemsets 248
5.3 Mining Various Kinds of Association Rules 250
5.3.1 Mining Multilevel Association Rules 250
5.3.2 Mining Multidimensional Association Rules
from Relational Databases and Data Warehouses 254
5.4 From Association Mining to Correlation Analysis 259
5.4.1 Strong Rules Are Not Necessarily Interesting: An Example 260
5.4.2 From Association Analysis to Correlation Analysis 261
5.5 Constraint-Based Association Mining 265
5.5.1 Metarule-Guided Mining of Association Rules 266
5.5.2 Constraint Pushing: Mining Guided by Rule Constraints 267
5.6 Summary 272
Exercises 274
Bibliographic Notes 280
Contents xiii
Chapter 6 Classification and Prediction 285
6.1 What Is Classification? What Is Prediction? 285
6.2 Issues Regarding Classification and Prediction 289
6.2.1 Preparing the Data for Classification and Prediction 289
6.2.2 Comparing Classification and Prediction Methods 290
6.3 Classification by Decision Tree Induction 291
6.3.1 Decision Tree Induction 292
6.3.2 Attribute Selection Measures 296
6.3.3 Tree Pruning 304
6.3.4 Scalability and Decision Tree Induction 306
6.4 Bayesian Classification 310
6.4.1 Bayes?Theorem 310
6.4.2 Na飗e Bayesian Classification 311
6.4.3 Bayesian Belief Networks 315
6.4.4 Training Bayesian Belief Networks 317
6.5 Rule-Based Classification 318
6.5.1 Using IF-THEN Rules for Classification 319
6.5.2 Rule Extraction from a Decision Tree 321
6.5.3 Rule Induction Using a Sequential Covering Algorithm 322
6.6 Classification by Backpropagation 327
6.6.1 A Multilayer Feed-Forward Neural Network 328
6.6.2 Defining a Network Topology 329
6.6.3 Backpropagation 329
6.6.4 Inside the Black Box: Backpropagation and Interpretability 334
6.7 Support Vector Machines 337
6.7.1 The Case When the Data Are Linearly Separable 337
6.7.2 The Case When the Data Are Linearly Inseparable 342
6.8 Associative Classification: Classification by Association
Rule Analysis 344
6.9 Lazy Learners (or Learning from Your Neighbors) 347
6.9.1 k-Nearest-Neighbor Classifiers 348
6.9.2 Case-Based Reasoning 350
6.10 Other Classification Methods 351
6.10.1 Genetic Algorithms 351
6.10.2 Rough Set Approach 351
6.10.3 Fuzzy Set Approaches 352
6.11 Prediction 354
6.11.1 Linear Regression 355
6.11.2 Nonlinear Regression 357
6.11.3 Other Regression-Based Methods 358
xiv Contents
6.12 Accuracy and Error Measures 359
6.12.1 Classifier Accuracy Measures 360
6.12.2 Predictor Error Measures 362
6.13 Evaluating the Accuracy of a Classifier or Predictor 363
6.13.1 Holdout Method and Random Subsampling 364
6.13.2 Cross-validation 364
6.13.3 Bootstrap 365
6.14 Ensemble Methods桰ncreasing the Accuracy 366
6.14.1 Bagging 366
6.14.2 Boosting 367
6.15 Model Selection 370
6.15.1 Estimating Confidence Intervals 370
6.15.2 ROC Curves 372
6.16 Summary 373
Exercises 375
Bibliographic Notes 378
Chapter 7 Cluster Analysis 383
7.1 What Is Cluster Analysis? 383
7.2 Types of Data in Cluster Analysis 386
7.2.1 Interval-Scaled Variables 387
7.2.2 Binary Variables 389
7.2.3 Categorical, Ordinal, and Ratio-Scaled Variables 392
7.2.4 Variables of Mixed Types 395
7.2.5 Vector Objects 397
7.3 A Categorization of Major Clustering Methods 398
7.4 Partitioning Methods 401
7.4.1 Classical Partitioning Methods: k-Means and k-Medoids 402
7.4.2 Partitioning Methods in Large Databases: From
k-Medoids to CLARANS 407
7.5 Hierarchical Methods 408
7.5.1 Agglomerative and Divisive Hierarchical Clustering 408
7.5.2 BIRCH: Balanced Iterative Reducing and Clustering
Using Hierarchies 412
7.5.3 ROCK: A Hierarchical Clustering Algorithm for
Categorical Attributes 414
7.5.4 Chameleon: A Hierarchical Clustering Algorithm
Using Dynamic Modeling 416
7.6 Density-Based Methods 418
7.6.1 DBSCAN: A Density-Based Clustering Method Based on
Connected Regions with Sufficiently High Density 418
Contents xv
7.6.2 OPTICS: Ordering Points to Identify the Clustering
Structure 420
7.6.3 DENCLUE: Clustering Based on Density
Distribution Functions 422
7.7 Grid-Based Methods 424
7.7.1 STING: STatistical INformation Grid 425
7.7.2 WaveCluster: Clustering Using Wavelet Transformation 427
7.8 Model-Based Clustering Methods 429
7.8.1 Expectation-Maximization 429
7.8.2 Conceptual Clustering 431
7.8.3 Neural Network Approach 433
7.9 Clustering High-Dimensional Data 434
7.9.1 CLIQUE: A Dimension-Growth Subspace Clustering Method 436
7.9.2 PROCLUS: A Dimension-Reduction Subspace Clustering
Method 439
7.9.3 Frequent Pattern朆ased Clustering Methods 440
7.10 Constraint-Based Cluster Analysis 444
7.10.1 Clustering with Obstacle Objects 446
7.10.2 User-Constrained Cluster Analysis 448
7.10.3 Semi-Supervised Cluster Analysis 449
7.11 Outlier Analysis 451
7.11.1 Statistical Distribution-Based Outlier Detection 452
7.11.2 Distance-Based Outlier Detection 454
7.11.3 Density-Based Local Outlier Detection 455
7.11.4 Deviation-Based Outlier Detection 458
7.12 Summary 460
Exercises 461
Bibliographic Notes 464
Chapter 8 Mining Stream, Time-Series, and Sequence Data 467
8.1 Mining Data Streams 468
8.1.1 Methodologies for Stream Data Processing and
Stream Data Systems 469
8.1.2 Stream OLAP and Stream Data Cubes 474
8.1.3 Frequent-Pattern Mining in Data Streams 479
8.1.4 Classification of Dynamic Data Streams 481
8.1.5 Clustering Evolving Data Streams 486
8.2 Mining Time-Series Data 489
8.2.1 Trend Analysis 490
8.2.2 Similarity Search in Time-Series Analysis 493
xvi Contents
8.3 Mining Sequence Patterns in Transactional Databases 498
8.3.1 Sequential Pattern Mining: Concepts and Primitives 498
8.3.2 Scalable Methods for Mining Sequential Patterns 500
8.3.3 Constraint-Based Mining of Sequential Patterns 509
8.3.4 Periodicity Analysis for Time-Related Sequence Data 512
8.4 Mining Sequence Patterns in Biological Data 513
8.4.1 Alignment of Biological Sequences 514
8.4.2 Hidden Markov Model for Biological Sequence Analysis 518
8.5 Summary 527
Exercises 528
Bibliographic Notes 531
Chapter 9 Graph Mining, Social Network Analysis, and Multirelational
Data Mining 535
9.1 Graph Mining 535
9.1.1 Methods for Mining Frequent Subgraphs 536
9.1.2 Mining Variant and Constrained Substructure Patterns 545
9.1.3 Applications: Graph Indexing, Similarity Search, Classification,
and Clustering 551
9.2 Social Network Analysis 556
9.2.1 What Is a Social Network? 556
9.2.2 Characteristics of Social Networks 557
9.2.3 Link Mining: Tasks and Challenges 561
9.2.4 Mining on Social Networks 565
9.3 Multirelational Data Mining 571
9.3.1 What Is Multirelational Data Mining? 571
9.3.2 ILP Approach to Multirelational Classification 573
9.3.3 Tuple ID Propagation 575
9.3.4 Multirelational Classification Using Tuple ID Propagation 577
9.3.5 Multirelational Clustering with User Guidance 580
9.4 Summary 584
Exercises 586
Bibliographic Notes 587
Chapter 10 Mining Object, Spatial, Multimedia, Text, andWeb Data 591
10.1 Multidimensional Analysis and Descriptive Mining of Complex
Data Objects 591
10.1.1 Generalization of Structured Data 592
10.1.2 Aggregation and Approximation in Spatial and Multimedia Data
Generalization 593
Contents xvii
10.1.3 Generalization of Object Identifiers and Class/Subclass
Hierarchies 594
10.1.4 Generalization of Class Composition Hierarchies 595
10.1.5 Construction and Mining of Object Cubes 596
10.1.6 Generalization-Based Mining of Plan Databases by
Divide-and-Conquer 596
10.2 Spatial Data Mining 600
10.2.1 Spatial Data Cube Construction and Spatial OLAP 601
10.2.2 Mining Spatial Association and Co-location Patterns 605
10.2.3 Spatial Clustering Methods 606
10.2.4 Spatial Classification and Spatial Trend Analysis 606
10.2.5 Mining Raster Databases 607
10.3 Multimedia Data Mining 607
10.3.1 Similarity Search in Multimedia Data 608
10.3.2 Multidimensional Analysis of Multimedia Data 609
10.3.3 Classification and Prediction Analysis of Multimedia Data 611
10.3.4 Mining Associations in Multimedia Data 612
10.3.5 Audio and Video Data Mining 613
10.4 Text Mining 614
10.4.1 Text Data Analysis and Information Retrieval 615
10.4.2 Dimensionality Reduction for Text 621
10.4.3 Text Mining Approaches 624
10.5 Mining theWorld WideWeb 628
10.5.1 Mining the Web Page Layout Structure 630
10.5.2 Mining the Web抯 Link Structures to Identify
Authoritative Web Pages 631
10.5.3 Mining Multimedia Data on the Web 637
10.5.4 Automatic Classification of Web Documents 638
10.5.5 Web Usage Mining 640
10.6 Summary 641
Exercises 642
Bibliographic Notes 645
Chapter 11 Applications and Trends in Data Mining 649
11.1 Data Mining Applications 649
11.1.1 Data Mining for Financial Data Analysis 649
11.1.2 Data Mining for the Retail Industry 651
11.1.3 Data Mining for the Telecommunication Industry 652
11.1.4 Data Mining for Biological Data Analysis 654
11.1.5 Data Mining in Other Scientific Applications 657
11.1.6 Data Mining for Intrusion Detection 658
xviii Contents
11.2 Data Mining System Products and Research Prototypes 660
11.2.1 How to Choose a Data Mining System 660
11.2.2 Examples of Commercial Data Mining Systems 663
11.3 Additional Themes on Data Mining 665
11.3.1 Theoretical Foundations of Data Mining 665
11.3.2 Statistical Data Mining 666
11.3.3 Visual and Audio Data Mining 667
11.3.4 Data Mining and Collaborative Filtering 670
11.4 Social Impacts of Data Mining 675
11.4.1 Ubiquitous and Invisible Data Mining 675
11.4.2 Data Mining, Privacy, and Data Security 678
11.5 Trends in Data Mining 681
11.6 Summary 684
Exercises 685
Bibliographic Notes 687
Appendix An Introduction to Microsoft抯 OLE DB for
Data Mining 691
A.1 Model Creation 693
A.2 Model Training 695
A.3 Model Prediction and Browsing 697
Bibliography 703

Absolute Beginner's Guide to Databases

2008-06-08 16:08:23

Editorial Reviews

Product Description

Absolute Beginner's Guide to Databases brings the elements of a database together using easy to understand language, perfect for the true beginner. It not only gives specific hands on practice, but also provides an overview of designing, maintaining and using a database. This book covers what databases are used for, why databases are important, why the design of the database is important, database normalization, keys to solid database design, differences in types of databases, and indexes--what they are, how we use them, and why they are important.

From the Back Cover
Absolute Beginner's Guide to Databases brings the elements of a database together using easy to understand language, perfect for the true beginner. It not only gives specific hands on practice, but also provides an overview of designing, maintaining and using a database. This book covers what databases are used for, why databases are important, why the design of the database is important, database normalization, keys to solid database design, differences in types of databases, and indexeswhat they are, how we use them, and why they are important.

Interscience Knowledge Based Clustering : From Data to Information Granules

2008-06-06 15:27:06

Editorial Reviews

Review
"I agree with Zadeh's opinion (mentioned at the end of book's foreword): 'The author and the publisher deserve our loud applause and congratulations.'" (Computing Reviews.com, May 19, 2005)

Product Description

A comprehensive coverage of emerging and current technology dealing with heterogeneous sources of information, including data, design hints, reinforcement signals from external datasets, and related topics
Covers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangible
Includes illustrative material andwell-known experimentsto offer hands-on experience

Preface

Data and patterns are an integral part of the cultural fabric of our information
society. The challenge we are confronted with every day is to cope with the
flood of data generated by banking transactions, millions of sensors, World Wide
Web log records, communication traffic of cellular calls, satellite image collection
systems, and networks of intelligent home appliances, to name just a few
evident examples.
Making sense of data has become a critical objective of intelligent data analysis
(IDA), data mining (DM), sensor fusion, image understanding, and logic-driven
system modeling. As never before, we are faced with the growing need to
construct a powerful computer “eye”—a human-centric, human-interactive, and
human-sensitive computer environment that helps us understand data and make
sensible decisions.
Clustering is one of the well-established manifestations of such a computer
eye. With its agenda of venturing into data spaces and discovering their structure—
clusters of data—clustering is an ideal vehicle for exploration of vast
territories of data spaces. From the early concepts of the 1930s, this field has
recently undergone a rapid expansion fueled by new conceptual and computing
challenges. The omnipresence of clustering today is astonishing. Even a
quick and fairly unsophisticated search of the Web or a simple search of any
library database returns thousands of hits revealing an impressive breadth of
applications: from biomedicine to marketing, engineering, economics, biological
sciences, chemistry, military, food engineering, finance, and education.
Clustering has become a synonym for a diversified suite of methodologies and
algorithms that are almost exclusively data-driven and in which any optimization
is predominantly, if not exclusively, data-oriented. Clustering gives rise to
a variety of information granules whose use reveals the structure of data. The
formalisms of granular computing help design clustering methods designed to
meet user-defined objectives. In this diversified landscape of clustering, the algorithms
operating within the framework of fuzzy sets have assumed an important
and unique position. The reason is obvious: fuzzy sets regarded as basic information
granules are human-centric. Dealing with concepts and groups (clusters) that
allow for partial membership is highly appealing. Identifying data (patterns) that
are of borderline character and may require special attention as potential outliers
is a useful value-added feature of fuzzy clustering. Discovering the most typical
patterns (with the highest membership values) in the cluster is another important
feature of by fuzzy sets.
In light of the recent applications and new forms of agent-based technology,
Web-based pursuits, and rapidly growing dimensionality and heterogeneity of
data sets, the human-centricity of clustering has become even more essential.
The paradigm of data-centric clustering has to be augmented. The paradigm of
knowledge-based clustering I introduce in this book is concerned with reconciling
two important driving forces of clustering activities: gaining data and domain
knowledge and building a coherent platform of navigation in highly dimensional
and often heterogeneous data spaces. The user plays a basic role in forming an
essential feedback loop in any highly interactive data analysis. Needless to say, we
require a carefully selected conceptual and algorithmic layer of human-machine
communication.
This book is divided into three parts. The first parts consisting of Chapters 1
to 3, provides a concise, carefully structured introduction to the subject. Three
interrelated components are presented. First, I discuss the fundamentals of fuzzy
clustering. Second, I review fuzzy computing, regarded as an important realization
of granular computing, focused on the issues of fuzzy clustering. Third, I
elaborate on the logic-based neurons and ensuing neural networks. The core of
the book, Chapters 4 to 10, presents a highly diversified landscape of knowledgebased
clustering. The third part of the book, consisting of Chapters 11 to 15, is
devoted to generic models whose design is directly linked to the paradigm of
knowledge-based clustering. First, I concentrate on hyperbox models of clusters,
demonstrating how the essential structure can be captured in terms of
hyperbox geometry. This is followed by studies of granular mappings and linguistic
models.
Throughout the book, I adhere to the standard notation used in pattern recognition
and system analysis, as well as the standard terminology used there. The
terms “data” and “pattern” are used interchangeably to emphasize the unified
way of treating various forms of pattern recognition, system modeling, and data
analysis. The book is self-contained. While the reader can benefit from some initial
familiarity with computational Intelligence (CI), this is not a must. CI helps
place the material in perspective and allows the reader to fully appreciate the
ideas of information granularity and information granules as building blocks of
various CI architectures.
The purpose of this book is to present the main ideas in a fairly general format
and not to skew the subject by limiting the discussion to selected application
areas. The algorithmic aspects are also kept quite general, and no attempt is made
to strive for the most efficient yet intricate implementations possible. This makes
the book of interest to a broad audience. Those readers interested in clustering,
fuzzy clustering, unsupervised learning, neural networks, fuzzy sets, and pattern
recognition, as well as those involved in numerous tasks of data analysis, will find
the book thought-provoking and intellectually stimulating. Readers involved in
system modeling will view knowledge-driven clustering as an attractive vehicle
of rapid prototyping of granular models.
Knowledge-based clustering has already emerged. This book outlines its fundamentals,
presents the essential algorithmic developments, and discusses its
application-driven aspects. No attempt has been made to cover the subject completely.
However, the material selected paints a coherent picture of the most
recent developments central to this rapidly evolving area.
Witold Pedrycz

Contents

Foreword xiii
Preface xv
1 Clustering and Fuzzy Clustering 1
1.1 Introduction 1
1.2 Basic Notions and Notation 1
1.2.1 Types of Data 2
1.2.2 Distance and Similarity 2
1.3 Main Categories of Clustering Algorithms 6
1.3.1 Hierarchical Clustering 6
1.3.2 Objective Function-Based Clustering 8
1.4 Clustering and Classification 10
1.5 Fuzzy Clustering 11
1.6 Cluster Validity 18
1.7 Extensions of Objective Function-Based Fuzzy Clustering 19
1.7.1 Augmented Geometry of Fuzzy Clusters: Fuzzy C
Varieties 19
1.7.2 Possibilistic Clustering 20
1.7.3 Noise Clustering 22
1.8 Self-Organizing Maps and Fuzzy Objective Function-Based
Clustering 23
1.9 Conclusions 25
References 26
2 Computing with Granular Information: Fuzzy Sets
and Fuzzy Relations 28
2.1 A Paradigm of Granular Computing: Information Granules
and Their Processing 28
2.2 Fuzzy Sets as Human-Centric Information Granules 31
2.3 Operations on Fuzzy Sets 32
2.4 Fuzzy Relations 33
2.5 Comparison of Two Fuzzy Sets 35
2.6 Generalizations of Fuzzy Sets 37
2.7 Shadowed Sets 38
2.8 Rough Sets 44
2.9 Granular Computing and Distributed Processing 46
2.10 Conclusions 47
References 47
3 Logic-Oriented Neurocomputing 50
3.1 Introduction 50
3.2 Main Categories of Fuzzy Neurons 51
3.2.1 Aggregative Neurons 52
3.2.2 Referential (Reference) Neurons 55
3.3 Architectures of Logic Networks 59
3.4 Interpretation Aspects of the Networks 61
3.5 Granular Interfaces of Logic Processing 62
3.6 Conclusions 64
References 64
4 Conditional Fuzzy Clustering 66
4.1 Introduction 66
4.2 Problem Statement: Context Fuzzy Sets and Objective
Function 68
4.3 The Optimization Problem 70
4.4 Computational Considerations of Conditional Clustering 80
4.5 Generalizations of the Algorithm Through the Aggregation
Operator 81
4.6 Fuzzy Clustering with Spatial Constraints 82
4.7 Conclusions 86
References 86
5 Clustering with Partial Supervision 87
5.1 Introduction 87
5.2 Problem Formulation 88
5.3 Design of the Clusters 90
5.4 Experimental Examples 91
5.5 Cluster-Based Tracking Problem 93
5.6 Conclusions 96
References 96
6 Principles of Knowledge-Based Guidance in Fuzzy Clustering 97
6.1 Introduction 97
6.2 Examples of Knowledge-Oriented Hints and Their General
Taxonomy 99
6.3 The Optimization Environment of Knowledge-Enhanced
Clustering 102
6.4 Quantification of Knowledge-Based Guidance Hints and
Their Optimization 105
6.5 Organization of the Interaction Process 107
6.6 Proximity-Based Clustering (P-FCM) 112
6.7 Web Exploration and P-FCM 117
6.8 Linguistic Augmentation of Knowledge-Based Hints 126
6.9 Conclusions 127
References 127
7 Collaborative Clustering 129
7.1 Introduction and Rationale 129
7.2 Horizontal and Vertical Clustering 131
7.3 Horizontal Collaborative Clustering 132
7.3.1 Optimization Details 135
7.3.2 The Flow of Computing of Collaborative
Clustering 137
7.3.3 Quantification of the Collaborative Phenomenon of
Clustering 138
7.4 Experimental Studies 140
7.5 Further Enhancements of Horizontal Clustering 150
7.6 The Algorithm of Vertical Clustering 151
7.7 A Grid Model of Horizontal and Vertical Clustering 153
7.8 Consensus Clustering 155
7.9 Conclusions 157
References 157
8 Directional Clustering 158
8.1 Introduction 158
8.2 Problem Formulation 159
8.2.1 The Objective Function 160
8.2.2 The Logic Transformation Between Information
Granules 161
8.3 The Algorithm 163
8.4 The Development Framework of Directional Clustering 166
8.5 Numerical Studies 167
8.6 Conclusions 174
References 176
9 Fuzzy Relational Clustering 178
9.1 Introduction and Problem Statement 178
9.2 FCM for Relational Data 179
9.3 Decomposition of Fuzzy Relational Patterns 181
9.3.1 Gradient-Based Solution to the Decomposition
Problem 182
9.3.2 Neural Network Model of the Decomposition
Problem 184
9.4 Comparative Analysis 188
9.5 Conclusions 189
References 189
10 Fuzzy Clustering of Heterogeneous Patterns 191
10.1 Introduction 191
10.2 Heterogeneous Data 192
10.3 Parametric Models of Granular Data 194
10.4 Parametric Mode of Heterogeneous Fuzzy Clustering 195
10.5 Nonparametric Heterogeneous Clustering 198
10.5.1 A Frame of Reference 198
10.5.2 Representation of Granular Data Through the
Possibility-Necessity Transformation 200
10.5.3 Dereferencing 205
10.6 Conclusions 207
References 208
11 Hyperbox Models of Granular Data: The Tchebyschev FCM 209
11.1 Introduction 209
11.2 Problem Formulation 210
11.3 The Clustering Algorithm—Detailed Considerations 211
11.4 Development of Granular Prototypes 218
11.5 Geometry of Information Granules 220
11.6 Granular Data Description: A General Model 223
11.7 Conclusions 223
References 224
12 Genetic Tolerance Fuzzy Neural Networks 226
12.1 Introduction 226
12.2 Operations of Thresholding and Tolerance: Fuzzy
Logic–Based Generalizations 227
12.3 Topology of the Logic Network 231
12.4 Genetic Optimization 235
12.5 Illustrative Numeric Studies 236
12.6 Conclusions 244
References 245
13 Granular Prototyping 246
13.1 Introduction 246
13.2 Problem Formulation 247
13.2.1 Expressing Similarity Between Two Fuzzy Sets 247
13.2.2 Performance Index (Objective Function) 248
13.3 Prototype Optimization 251
13.4 Development of Granular Prototypes 263
13.4.1 Optimization of the Similarity Levels 263
13.4.2 An Inverse Similarity Problem 264
13.5 Conclusions 268
References 268
14 Granular Mappings 270
14.1 Introduction and Problem Statement 270
14.2 Possibility and Necessity Measures as the Computational
Vehicles of Granular Representation 271
14.3 Building the Granular Mapping 272
14.4 Designing Multivariable Granular Mappings Through
Fuzzy Clustering 275
14.5 Quantification of Granular Mappings 278
14.6 Experimental Studies 278
14.7 Conclusions 280
References 282
15 Linguistic Modeling 283
15.1 Introduction 283
15.2 Cluster-Based Representation of Input-Output Mapping 285
15.3 Conditional Clustering in the Development of a Blueprint
of Granular Models 287
15.4 The Granular Neuron as a Generic Processing Element in
Granular Networks 290
15.5 The Architecture of Linguistic Models Based on
Conditional Fuzzy Clustering 293
15.6 Refinements of Linguistic Models 294
15.7 Conclusions 295
References 296
Bibliography 297
Index 315

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

2008-04-24 19:13:59

Chapter 1
Foundations and Ideas 3
1.1 Enterprise Applications and Analysis Models 4
1.2 Distributed and Centralized Repositories 8
1.3 The Age of Distributed Knowledge 11
1.4 Information and Knowledge Discovery 12
1.5 Data Mining and Business Models 18
1.6 Fuzzy Systems for Business Process Models 23
1.7 Evolving Distributed Fuzzy Models 25
1.8 A Sample Case: Evolving a Model for Customer Segmentation 26
1.9 Review 29
vii
viii ■ Contents
■ ■ ■ Chapter 2
Principal Model Types 31
2.1 Model and Event State Categorization 34
2.2 Model Type and Outcome Categorization 35
2.3 Review 36
■ ■ ■ Chapter 3
Approaches to Model Building 37
3.1 Ordinary Statistics 37
3.2 Nonparametric Statistics 38
3.3 Linear Regression in Statistical Models 40
3.4 Nonlinear Growth Curve Fitting 44
3.5 Cluster Analysis 47
3.6 Decision Trees and Classifiers 48
3.7 Neural Networks 51
3.8 Fuzzy SQL Systems 52
3.9 Rule Induction and Dynamic Fuzzy Models 55
3.10 Review 62
Further Reading 63
Part II Fuzzy Systems
■ ■ ■ Chapter 4
Fundamental Concepts of Fuzzy Logic 67
4.1 The Vocabulary of Fuzzy Logic 68
4.2 Boolean (Crisp) Sets: The Law of Bivalence 72
4.3 Fuzzy Sets 76
4.4 Review 93
Further Reading 94
■ ■ ■ Chapter 5
Fundamental Concepts of Fuzzy Systems 95
5.1 The Vocabulary of Fuzzy Systems 96
Contents ■ ix
5.2 Fuzzy Rule-based Systems: An Overview 100
5.3 Variable Decomposition into Fuzzy Sets 104
5.4 A Fuzzy Knowledge Base: The Details 113
5.5 The Fuzzy Inference Engine 119
5.6 Inference Engine Approaches 122
5.7 Running a Fuzzy Model 124
5.8 Review 147
■ ■ ■ Chapter 6
Fuzzy SQL and Intelligent Queries 149
6.1 The Vocabulary of Relational Databases and Queries 150
6.2 Basic Relational Database Concepts 156
6.3 Structured Query Language Fundamentals 159
6.4 Precision and Accuracy 161
6.5 Why We Search Databases 162
6.6 Expanding the Query Scope 165
6.7 Fuzzy Query Fundamentals 169
6.8 Measuring Query Compatibility 180
6.9 Complex Query Compatibility Metrics 184
6.10 Compatibility Threshold Management 187
6.11 Fuzzy SQL Process Flow 188
6.12 Fuzzy SQL Example 193
6.13 Evaluating Fuzzy SQL Outcomes 200
6.14 Review 204
Further Reading 205
■ ■ ■ Chapter 7
Fuzzy Clustering 207
7.1 The Vocabulary of Fuzzy Clustering 208
7.2 Principles of Cluster Detection 210
7.3 Some General Clustering Concepts 211
7.4 Crisp Clustering Techniques 218
7.5 Fuzzy Clustering Concepts 223
7.6 Fuzzy c-Means Clustering 228
7.7 Fuzzy Adaptive Clustering 248
x ■ Contents
7.8 Generating Rule Prototypes 259
7.9 Review 262
Further Reading 263
■ ■ ■ Chapter 8
Fuzzy Rule Induction 265
8.1 The Vocabulary of Rule Induction 266
8.2 Rule Induction and Fuzzy Models 269
8.3 The Rule Induction Algorithm 273
8.4 The Model Building Methodology 283
8.5 A Rule Induction and Model Building Example 288
8.6 Measuring Model Robustness 312
8.7 Technical Implementation 323
8.8 External Controls 325
8.9 Organization of the Knowledge Base 333
8.10 Review 337
Further Reading 338
Part III Evolutionary Strategies
■ ■ ■ Chapter 9
Fundamental Concepts of Genetic Algorithms 343
9.1 The Vocabulary of Genetic Algorithms 344
9.2 Overview 353
9.3 The Architecture of a Genetic Algorithm 365
9.4 Practical Issues in Using a Genetic Algorithm 413
9.5 Review 418
Further Reading 419
■ ■ ■ Chapter 10
Genetic Resource Scheduling Optimization 421
10.1 The Vocabulary of Resource-constrained Scheduling 421
10.2 Some Terminology Issues 425
Contents ■ xi
10.3 Fundamentals 426
10.4 Objective Functions and Constraints 428
10.5 Bringing It All Together: Constraint Scheduling 434
10.6 A Genetic Crew Scheduler Architecture 440
10.7 Implementing and Executing the Crew Scheduler 444
10.8 Topology Constraint Algorithms and Techniques 460
10.9 Adaptive Parameter Optimization 475
10.10 Review 479
Further Reading 480
■ ■ ■ Chapter 11
Genetic Tuning of Fuzzy Models 483
11.1 The Genetic Tuner Process 483
11.2 Configuration Parameters 488
11.3 Implementing and Running the Genetic Tuner 494
11.4 Advanced Genetic Tuning Issues 505
11.5 Review 515
Further Reading 516

Databases and Information Systems IV

2008-04-01 20:59:25

数据和信息系统
本刊物载有论文说，目前的结果原来的业务建模与企业工程，数据库研究，数据工程，数据质量和数据分析，是工程，网络工程，应用人工智能方法。捐款是由学者及教育工作者，从整个世界。编辑希望给出结果，将有助于进一步发展领域的研究数据库和信息系统。有些题目本刊物是：数据集成方法;数据挖掘;数据模型和数据库设计;分布式数据库技术和互操作;域本体与概念型搜索;信息系统和安全;数据仓库，联机分析，知识发现;信息系统电脑，移动设备和代理商;综合信息系统;智能信息经纪人;知识表示和知识工程;知识管理;语言成分的信息系统;逻辑和数据库;与元和模型驱动架构。
This publication contains papers that present original results in business modeling and enterprise engineering, database research, data engineering, data quality and data analysis, IS engineering, Web engineering, and application of AI methods. The contributions are from academics and practitioners from the entire world. The editors hope that the presented results will contribute to the further development of research in the field of databases and information systems. Some topics of this publication are: Data Integration Methods; Data Mining; Data Models and Database Design; Distributed Database Techniques and Interoperability; Domain Ontologies and Concept-Based Search; Information Systems and Security; Data Warehousing, OLAP, and Knowledge Discovery; Information Systems, Mobile Computing, and Agents; Integrated Information Systems; Intelligent Information Agents; Knowledge Representation and Knowledge Engineering; Knowledge Management; Linguistic Components of Information Systems; Logic and Databases; and Metamodels and Model Driven Architecture.

Contents
Conference Committee v
Preface vii
Olegas Vasilecas, Johann Eder and Albertas Caplinskas
Invited Papers
Events and Rules for Java: Using a Seamless and Dynamic Approach 3
Sharma Chakravarthy, Rajesh Dasari, Sridhar Varakala and
Raman Adaikkalavan
On Ontology, Ontologies, Conceptualizations, Modeling Languages, and
(Meta)Models 18
Giancarlo Guizzardi
Pattern Repositories for Software Engineering Education 40
Hans-Werner Sehring, Sebastian Bossung, Patrick Hupe, Michael Skusa
and Joachim W. Schmidt
Business Modeling and Enterprise Engineering
A Technique for the Prediction of Deadline-Violations in Inter-Organizational
Business Processes 57
Johann Eder, Horst Pichler and Stefan Vielgut
Interactions at the Enterprise Knowledge Management Layer 72
Saulius Gudas and Rasa Brundzaite
Databases
Methods for a Synchronised Evolution of Databases and Associated Ontologies 89
Andreas Kupfer, Silke Eckstein, Britta St?rmann, Karl Neumann and
Brigitte Mathiak
A Heuristic Approach to Fragmentation Incorporating Query Information 103
Hui Ma, Klaus-Dieter Schewe and Markus Kirchberg
Introducing Softness into Inductive Queries on String Databases 117
Ieva Mitasiunaite and Jean-Fran?ois Boulicaut
Data Engineering
A Multiple Correspondence Analysis to Organize Data Cubes 133
Riadh Ben Messaoud, Omar Boussaid and Sabine Loudcher Rabaséda
Model-Driven Development for Enabling the Feadback from Warehouses and
OLAP to Operational Systems 147
Lina Nemuraite, Jurgita Tonkunaite and Bronius Paradauskas
Data Quality and Data Analysis
A Time-Series Representation for Temporal Web Mining Using a Data Band
Approach 161
Mireille Samia and Stefan Conrad
The Framework for Business Rule Based Software Modeling: An Approach
for Data Analysis Models Integration 175
Olegas Vasilecas and Aidas Smaizys
IS Engineering
Aspect-Oriented Use Cases and Crosscutting Interfaces for Reconfigurable
Behavior Modeling 189
Lina Nemuraite and Milda Balandyte
Integrated Enterprise Information Systems: Thinking in Component Concepts 203
Audrone Lupeikiene
Web Engineering
A Web Service-Oriented Architecture for Implementing Web Information
Systems 219
Flavius Frasincar, Geert-Jan Houben and Peter Barna
Adaptive Support of Inter-Domain Collaborative Protocols Using Web Services
and Software Agents 233
Adomas Svirskas, Michael Wilson, Bob Roberts and Ioannis Ignatiadis
Using a Rule Language for Capturing Semantics in Web-Based Systems 249
Tanel Tammet, Hele-Mai Haav, Vello Kadarpik and Marko K??ramees
Application of AI Methods
A Concept Map Based Intelligent System for Adaptive Knowledge Assessment 263
Alla Anohina and Janis Grundspenkis
Semantic Interoperability Between Functional Ontologies 277
Nacima Mellal, Richard Dapoigny and Laurent Foulloy
Principles of Model Driven Architecture in Knowledge Modeling for the
Task of Study Program Evaluation 291
Oksana Nikiforova, Marite Kirikova and Natalya Pavlova
Author Index 305

Database Modeling and Design Logical Design, 4th Edition

2008-04-01 20:54:16

数据库建模与设计：逻辑设计，第四版
数据库系统和数据库设计技术已经历了重大的演变，近年来关系数据模型和关系数据库系统主宰的商业应用;反过来，他们延长了其他技术，如数据仓库，联机分析，数据挖掘。你怎么模型与设计你的数据库应用，在考虑新的技术或新的业务需求？

在广泛修改第四版， youll得到明确的解释，有许多了不起的例子，一个典型个案，而真正实际的意见，你来算-与设计规则，适用于任何基于s ql的制度。但youll也得到很多帮助你成长，从一个新的数据库设计师资深设计师开发工业规模的系统。

+详细看看统一建模语言（ uml -2 ），以及实体关系（二）方法的数据规格和概念建模-实例整个书两种办法！
+细节和实例，说明如何使用数据模型概念，在逻辑数据库设计，并转化概念模型向关系模型，并到sql语法;
+基础数据库正常化通过第五范式;
+实用范围的重大问题的商务智能-数据仓库，联机分析，为决策支持系统，数据挖掘;
+举例如何使用最流行的case工具，以处理复杂的数据建模问题。
+演习测试了解所有材料，加上解许多练习。
Database systems and database design technology have undergone significant evolution in recent years. The relational data model and relational database systems dominate business applications; in turn, they are extended by other technologies like data warehousing, OLAP, and data mining. How do you model and design your database application in consideration of new technology or new business needs?

In the extensively revised fourth edition, youll get clear explanations, lots of terrific examples and an illustrative case, and the really practical advice you have come to count on--with design rules that are applicable to any SQL-based system. But youll also get plenty to help you grow from a new database designer to an experienced designer developing industrial-sized systems.

+ a detailed look at the Unified Modeling Language (UML-2) as well as the entity-relationship (ER) approach for data requirements specification and conceptual modeling--with examples throughout the book in both approaches!
+ the details and examples of how to use data modeling concepts in logical database design, and the transformation of the conceptual model to the relational model and to SQL syntax;
+ the fundamentals of database normalization through the fifth normal form;
+ practical coverage of the major issues in business intelligence--data warehousing, OLAP for decision support systems, and data mining;
+ examples for how to use the most popular CASE tools to handle complex data modeling problems.
+ Exercises that test understanding of all material, plus solutions for many exercises.

Contents
Preface xv
Chapter 1
Introduction 1
1.1 Data and Database Management 2
1.2 The Database Life Cycle 3
1.3 Conceptual Data Modeling 8
1.4 Summary 11
1.5 Literature Summary 11
Chapter 2
The Entity-Relationship Model 13
2.1 Fundamental ER Constructs 13
2.1.1 Basic Objects: Entities, Relationships, Attributes 13
2.1.2 Degree of a Relationship 16
2.1.3 Connectivity of a Relationship 18
2.1.4 Attributes of a Relationship 19
2.1.5 Existence of an Entity in a Relationship 19
2.1.6 Alternative Conceptual Data Modeling Notations 20
x Contents
2.2 Advanced ER Constructs 23
2.2.1 Generalization: Supertypes and Subtypes 23
2.2.2 Aggregation 25
2.2.3 Ternary Relationships 25
2.2.4 General n-ary Relationships 28
2.2.5 Exclusion Constraint 29
2.2.6 Referential Integrity 30
2.3 Summary 30
2.4 Literature Summary 31
Chapter 3
The Unified Modeling Language (UML) 33
3.1 Class Diagrams 34
3.1.1 Basic Class Diagram Notation 35
3.1.2 Class Diagrams for Database Design 37
3.1.3 Example from the Music Industry 43
3.2 Activity Diagrams 46
3.2.1 Activity Diagram Notation Description 46
3.2.2 Activity Diagrams for Workflow 48
3.3 Rules of Thumb for UML Usage 50
3.4 Summary 51
3.5 Literature Summary 51
Chapter 4
Requirements Analysis and Conceptual Data Modeling 53
4.1 Introduction 53
4.2 Requirements Analysis 54
4.3 Conceptual Data Modeling 55
4.3.1 Classify Entities and Attributes 56
4.3.2 Identify the Generalization Hierarchies 57
4.3.3 Define Relationships 58
4.3.4 Example of Data Modeling: Company Personnel and
Project Database 61
4.4 View Integration 66
4.4.1 Preintegration Analysis 67
4.4.2 Comparison of Schemas 68
4.4.3 Conformation of Schemas 68
Contents xi
4.4.4 Merging and Restructuring of Schemas 69
4.4.5 Example of View Integration 69
4.5 Entity Clustering for ER Models 74
4.5.1 Clustering Concepts 75
4.5.2 Grouping Operations 76
4.5.3 Clustering Technique 78
4.6 Summary 81
4.7 Literature Summary 82
Chapter 5
Transforming the Conceptual Data Model to SQL 83
5.1 Transformation Rules and SQL Constructs 83
5.1.1 Binary Relationships 85
5.1.2 Binary Recursive Relationships 90
5.1.3 Ternary and n-ary Relationships 92
5.1.4 Generalization and Aggregation 101
5.1.5 Multiple Relationships 103
5.1.6 Weak Entities 103
5.2 Transformation Steps 103
5.2.1 Entity Transformation 104
5.2.2 Many-to-Many Binary Relationship Transformation 104
5.2.3 Ternary Relationship Transformation 105
5.2.4 Example of ER-to-SQL Transformation 105
5.3 Summary 106
5.4 Literature Summary 106
Chapter 6
Normalization 107
6.1 Fundamentals of Normalization 107
6.1.1 First Normal Form 109
6.1.2 Superkeys, Candidate Keys, and Primary Keys 109
6.1.3 Second Normal Form 111
6.1.4 Third Normal Form 113
6.1.5 Boyce-Codd Normal Form 115
6.2 The Design of Normalized Tables: A Simple Example 116
6.3 Normalization of Candidate Tables Derived from
ER Diagrams 118
xii Contents
6.4 Determining the Minimum Set of 3NF Tables 122
6.5 Fourth and Fifth Normal Forms 127
6.5.1 Multivalued Dependencies 127
6.5.2 Fourth Normal Form 129
6.5.3 Decomposing Tables to 4NF 132
6.5.4 Fifth Normal Form 133
6.6 Summary 137
6.7 Literature Summary 138
Chapter 7
An Example of Logical Database Design 139
7.1 Requirements Specification 139
7.1.1 Design Problems 140
7.2 Logical Design 141
7.3 Summary 145
Chapter 8
Business Intelligence 147
8.1 Data Warehousing 148
8.1.1 Overview of Data Warehousing 148
8.1.2 Logical Design 152
8.2 Online Analytical Processing (OLAP) 166
8.2.1 The Exponential Explosion of Views 167
8.2.2 Overview of OLAP 169
8.2.3 View Size Estimation 170
8.2.4 Selection of Materialized Views 173
8.2.5 View Maintenance 176
8.2.6 Query Optimization 177
8.3 Data Mining 178
8.3.1 Forecasting 179
8.3.2 Text Mining 181
8.4 Summary 185
8.5 Literature Summary 186
Contents xiii
Chapter 9
CASE Tools for Logical Database Design 187
9.1 Introduction to the CASE Tools 188
9.2 Key Capabilities to Watch For 191
9.3 The Basics 192
9.4 Generating a Database from a Design 196
9.5 Database Support 199
9.6 Collaborative Support 200
9.7 Distributed Development 201
9.8 Application Life Cycle Tooling Integration 202
9.9 Design Compliance Checking 204
9.10 Reporting 206
9.11 Modeling a Data Warehouse 207
9.12 Semi-Structured Data, XML 209
9.13 Summary 211
9.14 Literature Summary 211
Appendix: The Basics of SQL 213
Glossary 231
References 239
Exercises 249
Solutions to Selected Exercises 259
About the Authors 263
Index 265

数据仓库(中文原书第四版)

2008-02-28 02:16:45

【内容简介】
本书系统讲述数据仓库的基本概念、基本原理以及建立数据仓库的方法和过程。主要内容包括：决策支持系统的发展、数据仓库环境结构、数据仓库设计、数据仓库粒度划分、数据仓库技术、分布式数据仓库、EIS系统和数据仓库的关系、外部和非结构化数据与数据仓库的关系、数据装裁问题、数据仓库与Web、ERP与数据仓库以及数据仓库设计的复查要目。
本书是数据仓库之父撰写的关于数据仓库的最权威著作，既可作为相关专业的研究生教材，也是数据仓库的研究、开发和管理人员的必备指南。

数据库原理课件

2008-02-15 19:52:22

ppt格式
目录：
Development History for Database
Database
Data Description of real world
data Model of Database
Relation Calculus
Tuple&Domain Relation Calculus
Data Manipulation languages
SQL Introduction & DDL
SQL and DML in SQL
Embedded SQL
Dynamic SQL
QBE Language
Optimitation of Query
Access_path Based Query Optimization
DBMS
DBMS_1
Recovery Introduction
Execution and Recovery of Update transaction
Concurrent Control Introduction
Locking Protocol
Examination dead lock
Multiple Granularit Locking
Concurrent Control Baseed Time Stamp

萨王数据库系统概论第三版

2007-12-14 11:55:10

本书是教育部“高等教育面向对世纪教学内容和课程体系改革计划”的研究成果，是面向21世纪课程教材和教育部高等学校计算机科学与技术学科“九五”规划教材。本书是在第二版基础上修订而成的，与第二版相比较，在整体结构上进行了适当的调整，增加了数据库新技术方面的内容。全书内容包括：数据库模型、数据库系统结构、关系数据库系统、 SQL语言、复杂数据理论、数据库维护、数据库设计、关系数据库管理系统、数据库技术新进展、面向对象数据库系统、分布式数据库系统、并行数据库系统等。本书曾获国家优秀教材奖，并且是教育部“九五”重点教材。本书可作为高等学校计算机有关专业的数据库课程教材，也可供从事计算机软件工作的科技人员和工程技术人员以及其他有关部门人员参阅。

Mathematical Methods for Knowledge Discovery and Data Mining

2007-11-26 12:57:09

知识发现与数据挖掘的数学方法

Mathematical Methods for Knowledge Discovery and Data Mining

Author: Giovanni Felici, Carlo Vercellis
Publisher: Idea Group Reference
Number Of Pages: 371
Publication Date: 2007-10-04
ISBN-10 / ASIN: 1599045281
ISBN-13 / EAN: 9781599045283
Binding: Hardcover

The field of data mining has seen a demand in recent years for the development of ideas and results in an integrated structure. Mathematical Methods for Knowledge Discovery & Data Mining focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; and many others. This Premier Reference Source is an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance and insurance, manufacturing, marketing, performance measurement, and telecommunications.

Emerging Technologies of Text Mining. Techniques and Applications

2007-11-26 12:20:04

文本挖掘新技术

Emerging Technologies of Text Mining. Techniques and Applications

Author: Hercules Antonio Do Prado, Edilson Ferneda
Publisher: Idea Group Reference
Number Of Pages: 358
Publication Date: 2007-10-10
ISBN-10 / ASIN: 1599043734
ISBN-13 / EAN: 9781599043739
Binding: Hardcover

Massive amounts of textual data make up most organizations stored information. Therefore, there is increasingly high demand for a comprehensive resource providing practical hands-on knowledge for real-world applications.

Emerging Technologies of Text Mining: Techniques and Applications provides the most recent technical information related to the computational models of the text mining process, discussing techniques within the realms of classification, association analysis, information extraction, and clustering. Offering an innovative approach to the utilization of textual information mining to maximize competitive advantage, Emerging Technologies of Text Mining: Techniques and Applications will provide libraries with the defining reference on this topic.

Data Mining with Ontologies. Implementations, Findings and Frameworks

2007-11-22 14:56:32

Data Mining with Ontologies. Implementations, Findings and Frameworks

Author: Héctor Oscar Nigro, Sandra Elizabeth Gonzalez Cisaro, Daniel Hugo Xodo
Publisher: Idea Group Reference
Publication Date: 2007-07-19
ISBN-10 / ASIN: 1599046180
ISBN-13 / EAN: 9781599046181
Binding: Hardcover

One of the most important and challenging problems in data mining is the definition of prior knowledge either from the process or the domain. Prior knowledge is helpful for selecting suitable data and mining techniques, pruning the space of hypothesis, representing the output in a comprehensible way, and improving the overall method. Data Mining with Ontologies: Implementations, Findings, and Frameworks provides a comprehensive set of methodologies and tools for the development of ontological foundations for data mining in diverse domains ranging from biomedicine to marketing. Forming a benchmark reference for future efforts to enhance capabilities in ontology utilization and design, this Premier Reference Source will be an invaluable addition to libraries worldwide. One of the most important and challenging problems in data mining is the definition of prior knowledge either from the process or the domain. Prior knowledge is helpful for selecting suitable data and mining techniques, pruning the space of hypothesis, representing the output in a comprehensible way, and improving the overall method. Data Mining with Ontologies: Implementations, Findings, and Frameworks provides a comprehensive set of methodologies and tools for the development of ontological foundations for data mining in diverse domains ranging from biomedicine to marketing. Forming a benchmark reference for future efforts to enhance capabilities in ontology utilization and design, this Premier Reference Source will be an invaluable addition to libraries worldwide.

Building The Data Ware house

2007-11-12 23:03:22

This book is for developers, managers, designers, data administrators, database
administrators, and others who are building systems in a modern data processing
environment. In addition, students of information processing will find this
book useful. Where appropriate, some discussions will be more technical. But,
for the most part, the book is about issues and techniques, and it is meant to
serve as a guideline for the designer and the developer.
There are, then, many major differences between the operational environment
and the analytical environment. This book is about the analytical, DSS environment
and addresses the following issues:
■■ Granularity of data
■■ Partitioning of data
■■ Meta data
■■ Lack of credibility of data
■■ Integration of DSS data
■■ The time basis of DSS data
■■ Identifying the source of DSS data-the system of record
■■ Migration and methodology

数据挖掘导论

2007-11-12 15:08:56

数据挖掘技术，又称数据库知识发现，是20世纪90年代在信息技术领域开始迅速兴起的计算机技术。作者结合自己10余年来所从事的专家系统、机器学习、数据发掘，以及互联网信息智能处理等方面科研与教学经验，编著完成了本书。
本书系统地介绍了数据挖掘中的主要挖掘方法和对复杂数据进行挖掘的方法，以及在互联网信息智能处理领域中，数据挖掘方法与技术的主要应用。
本书的主要内容包括：数据挖掘基本知识、数据挖掘处理流程、数据预处理方法、定性概念归纳、决策树分类方法、回归统计预测方法、关联规则发现方法、各种聚类算法，以及复杂数据，尤其是多媒体数据挖掘方法的最新研究成果；此外还详细介绍了利用数据挖掘方法获取互联网信息，挖掘互联网使用知识，以及网络安全中数据挖掘方法应用等。
本书作为学习、掌握和应用数据挖掘方法和技术的综合指导书，是从事数据挖掘研究与设计人员、开发人员，以及需要了解数据挖掘有关方法与技术的IT技术人员的良师益友。同时也是一本较好的大学高年级或研究生相关课程的教材和参考书。

Data Mining Patterns. New Methods and Applications

2007-11-06 16:27:22

Data Mining Patterns. New Methods and Applications

Author: pascal Poncelet, Maguelonne Teisseire, Florent Masseglia
Publisher: Idea Group Reference
Number Of Pages: 307
Publication Date: 2007-08-27
ISBN-10 / ASIN: 1599041626
ISBN-13 / EAN: 9781599041629
Binding: Hardcover

Since the introduction of the Apriori algorithm a decade ago, the problem of mining patterns is becoming a very active research area, and efficient techniques have been widely applied to the problems either in industry or science. Currently, the data mining community is focusing on new problems such as: mining new kinds of patterns, mining patterns under constraints, considering new kinds of complex data, and real-world applications of these concepts.

Data Mining Patterns: New Methods and Applications provides an overall view of the recent solutions for mining, and also explores new kinds of patterns. This book offers theoretical frameworks and presents challenges and their possible solutions concerning pattern extractions, emphasizing both research techniques and real-world applications. Data Mining Patterns: New Methods and Applications portrays research applications in data models, techniques and methodologies for mining patterns, multi-relational and multidimensional pattern mining, fuzzy data mining, data streaming, incremental mining, and many other topics.

数据仓库

2007-10-08 17:05:13

Encyclopedia of Data Warehousing and Mining (2006)

2007-09-28 18:32:28

There has been much interest developed in the data mining field both in the academia and the industry over the past
10-15 years. The number of researchers and practitioners working in the field and the number of scientific papers
published in various data mining outlets increased drastically over this period. Major commercial vendors incorporated
various data mining tools into their products, and numerous applications in many areas, including life sciences, finance,
CRM, and Web-based applications, have been developed and successfully deployed.
Moreover, this interest is no longer limited to the researchers working in the traditional fields of statistics, machine
learning and databases, but has recently expanded to other fields, including operations research/management science
(OR/MS) and mathematics, as evidenced from various data mining tracks organized at different INFORMS meetings,
special issues of OR/MS journals and the recent conference on Mathematical Foundations of Learning Theory
organized by mathematicians.

Data Modeling Fundamentals

2007-09-08 04:36:30

The purpose of this book is to provide a practical approach for IT
professionals to acquire the necessary knowledge and expertise in data
modeling to function effectively. It begins with an overview of basic
data modeling concepts, introduces the methods and techniques, provides
a comprehensive case study to present the details of the data model
components, covers the implementation of the data model with emphasis on
quality components, and concludes with a presentation of a realistic
approach to data modeling. It clearly describes how a generic data model
is created to represent truly the enterprise information requirements.

db2 9 基础知识文档汇总

2007-08-23 14:33:40

分布式系统领域最好的书籍 Distributed Systems-Concept and Design

2007-08-09 09:00:11

分布式系统领域最好的书籍。
开发数据库的必备参考资料。

1stOpt 用户手册

2007-08-07 11:22:38

数据挖掘技术：市场营销、销售与客户关系管理领域应用（原书第2版）

2007-07-31 08:15:08

【内容简介】
本书是一本优秀的数据挖掘教材，全面而系统地介绍了数据挖掘酌商业环境、数据挖掘技术及其在商业环境中的应用。.
全书共18章，内容涵盖核心的数据挖掘技术，包括：决策树、神经网络、协同过滤、关联规则、链接分析、聚类和生存分析等。此外，还提供了数据挖掘最佳实践的概观、数据挖掘的最新进展和一些极具挑战性的研究课题，极具技术深度与广度。通过学习本书，读者不仅可以精通数据挖掘的整体结构和核心技术，还可以领略数据挖掘在销售和客户关系管理等方面的成功应用，为实践数据挖掘打下坚实的基础。
本书适合作为高等院校相关专业高年级本科生或研究生的教材或参考书，也适合当前和未来的数据挖掘实践者学习和参考。..
本书是数据挖掘领域的经典著作，数年来畅销不衰。全书从技术和应用两个方面，全面、系统地介绍了数据挖掘的商业环境、数据挖掘技术及其在商业环境中的应用。自从1997年本书第1版出版以来，数据挖掘界发生了巨大的变化，其中的大部分核心算法仍然保持不变，但是算法嵌入的软件、应用算法的数据库以及用于解决的商业问题都有所演进。第2版展示如何利用基本的数据挖掘方法和技术，解决常见的商业问题。
本书涵盖核心的数据挖掘技术，包括：决策树、神经网络、协同过滤、关联规则、链接分析、聚类和生存分析等。此外，还提供了数据挖掘最佳实践、数据挖掘的最新进展和一些富有挑战性的研究课题，极具技术深度与广度。配套网站www．data-miners．com／companion提供了每章的练习和用于测试各种数据挖掘技术的数据。全书语句凝炼、清新，对复杂概念的实际应用进行了生动解释，是必不可少的数据挖掘教材。...

【目录信息】
出版者的话
专家指导委员会
译者序.
致谢
前言
第1章数据挖掘的缘起和内容
1．1 分析客户关系管理系统
1．2 什么是数据挖掘
1．3 数据挖掘可以完成哪些工作
1．4 为什么现在研究
1．5 目前如何使用数据挖掘
1．6 小结
第2章数据挖掘的良性循环
2．1 商业数据挖掘案例研究
2．2 何谓良性循环
2．3 良性循环环境下的数据挖掘
2．4 移动通信公司建立恰当的联系
2．5 神经网络和决策树驱动SUV的销售
2．6 小结
第3章数据挖掘方法论和最佳实践

Contemporary.Issues.in.Database.Design.and.Information.Systems.Development

2007-07-28 19:53:51

很多人合作写的数据库设计书籍

Principles of Transaction Processing

2007-07-27 17:57:46

经典数据库开发数据

浙江大学数据库技术配套资料-电子商务

2007-07-20 12:24:15

浙江大学数据库技术张军第11-12讲

2007-07-20 12:24:00

浙江大学数据库技术张军第10讲

2007-07-20 12:17:35

浙江大学数据库技术张军第09讲

2007-07-20 12:12:39

浙江大学数据库技术张军第07-08讲

2007-07-20 12:12:37

浙江大学数据库技术张军第06讲

2007-07-20 12:12:37

浙江大学数据库技术张军第05讲

2007-07-20 12:12:36

浙江大学数据库技术张军第03-04讲

2007-07-20 12:12:30

浙江大学数据库技术张军第02讲

2007-07-20 12:12:28

浙江大学数据库技术张军第01讲

2007-07-20 12:12:26

浙江大学数据库技术张军06夏复习课

2007-07-20 12:12:25

Building the Data Warehouse, 4th Edition（最权威的“数据仓库”经典著作，最新第4版）

2007-07-12 18:33:07

这可是最权威的《数据仓库》经典著作，权威“数据仓库”的定义都出自William H. Inmon，而且第4版也是最新的版本，在该版本中主要内容和修订的内容是：
The new edition of the classic bestseller that launched the data warehousing industry covers new approaches and technologies, many of which have been pioneered by Inmon himself
In addition to explaining the fundamentals of data warehouse systems, the book covers new topics such as methods for handling unstructured data in a data warehouse and storing data across multiple storage media
Discusses the pros and cons of relational versus multidimensional design and how to measure return on investment in planning data warehouse projects
Covers advanced topics, including data monitoring and testing
Although the book includes an extra 100 pages worth of valuable content.

GPS导航数据的组织与应用研究

2007-07-04 13:32:19

Introduction to Data Mining and its Applications

2007-07-03 09:49:44

数据挖掘方面的一本新书(2006年出版)，不但讲述了DM的基本原理，还讨论了DM算法及其演化过程，并以案例的形式给出了DM的具体应用，是一本不可多得的好书。

Springer出版社作为计算机方面的权威机构，也保证了本书的质量，希望大家喜欢

数据挖掘第35-36讲王灿

2007-07-02 13:59:53

Data Mining: Concepts and Techniques, Second Edition

2007-07-01 22:56:48

数据挖掘也算是一门热门技术了，现在几乎所有高校都是以这本书的第一版（2001年版）作为指定教材。其第二版在美国也是2006年才出版，作者对书的内容作了许多更新，并新增了好几章。相信它一定能给下载者带来不少收获。

作者是大陆出去的华人，现在加拿大当教授，曾获得IEEE的大奖，算是计算机界混得比较牛的华人之一了。作者是国际数据库和数据挖掘方面的大牛，曾提出了数据挖掘方面的一些经典算法--学过的人都知道

数据挖掘第13讲王灿

2007-06-21 14:41:05

数据挖掘第10讲王灿

2007-06-21 14:41:02

数据挖掘第08-09讲王灿

2007-06-21 14:40:55

数据挖掘第07讲王灿

2007-06-21 14:40:51

数据挖掘第05-06讲王灿

2007-06-21 14:40:42

数据挖掘第04讲王灿

2007-06-21 14:40:39