Data mining / Vikram Pusi and P. Radha Krishna

Chapter 1 Introduction
1.1 Motivation 7
1.2 Data Warehousing and Data Mining Technologies
1.3 Data Models 6
1.4 Data Warehousing and OLAP: User's Perspective
1.5 Data Mining: User's Perspective 77
1.6 Related Disciplines 14
1.7 Other Issues 77
1.8 Future Trends 20
Summary 20
Exercises 21
Chapter 2 Frequent Pattern Mining
Introduction 22
2.1 Basic Problem Definition 23
2.2 Mining Association Rules 25
2.3 Applications 27
2.4 Variations 35
2.5 Interestingness 39
2.6 Frequent Itemset Mining (FIM) Algorithms 43
2.7 Current Status of FIM Algorithm Comparison 66
2.8 Optimal FIM Algorithms 67
2.9 Incremental Mining 77
2.10 Conciseness of Results 78
2.11 Sequential Rules 80
Summary 81
Exercises 83
Chapter 3 Classification
Introduction 86
3.1 Basic Problem Definition 87
3.2 Applications 89
3.3 Evaluation of Classifiers 90
3.4 Other Issues 94
3.5 Classification Techniques 100
3.6 Optimal Classification Algorithms 117
3.7 Regression 121
Summary 123
Exercises 123
Chapter 4 Clusturing
Introduction 125
4.1 Basic Problem Definition 126
4.2 Clustering: Applications 128
4.3 Measurement of Similarity 130
4.4 Evaluation of Clustering Algorithms 135
4.5 Classification of Clustering Algorithms 138
4.6 Partitioning Methods 139
4.7 Hierarchical Methods 121
4.8 Density-based Methods 146
4.9 Grid-based Methods 158
4.10 Outlier Detection 159
Summary 162
Exercises 163
Chapter 5 Pattern Discovery in Real-World Data
Introduction 166
5.1 Relational Data 166
5.2 Transactional Data 170
5.3 Multi-Dimensional Data 176
5.4 Distributed Data 178
5.5 Spatial Data 181
5.6 Data Streams 183
5.7 Time-Series Data 191
5.8 Text and Web Data 194
5.9 Multimedia Data 203
Summary 205
Exercises 205
Chapters Data Warehousing: The Data Model
Introduction 207
6.1 Fundamentals 207
6.2 Data Warehouse Data Characteristics 216
6.3 Data Warehouse Components 219
6.4 Approaches to Build Data Marts and Data Warehouse 223
6.5 ETL 224
6.6 Logical Data Modeling 235
6.7 Schemas Design in Dimensional Modeling 244
6.8 CLAP 249
6.9 Storage and Chunks 258
Summary 266
Exercises 267
Chapter 7 Data Warehousing: Query Processing
Introduction 269
7.1 Materialized Views 271
7.2 Materialized Views Selection 275
7.3 Materialized View Maintenance and Consistency 281
7.4 Indexing 283
7.5 General Query Evaluation 294
Summary 299
Exercises 300
Chapter 8 Case Studies
Introduction 302
8.1 Study 1: Telecom Content Warehouse 3 03
8.2 Study 2: OLAP for the Fast Food Industry 309
8.3 Study 3: FYototype Credit DataMart for a Bank 310
8.4 Study 4: Chum Modeling for a Bank 317
8.5 Study 5: Intrusion Detection using kNN classification 325
Summary 329
Exercises 329
Chapter 9 Current Trends in Pattern Discovery
Introduction 330
9.1 Ten Challenging Problems 331
Summary 335