Data Mining: Concepts, Models, Methods, and Algorithms, 2nd EditionISBN: 9780470890455
552 pages
August 2011, WileyIEEE Press

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Preface to the First Edition xv
1 DATAMINING CONCEPTS 1
1.1 Introduction 1
1.2 DataMining Roots 4
1.3 DataMining Process 6
1.4 Large Data Sets 9
1.5 Data Warehouses for Data Mining 14
1.6 Business Aspects of Data Mining: Why a DataMining Project Fails 17
1.7 Organization of This Book 21
1.8 Review Questions and Problems 23
1.9 References for Further Study 24
2 PREPARING THE DATA 26
2.1 Representation of Raw Data 26
2.2 Characteristics of Raw Data 31
2.3 Transformation of Raw Data 33
2.4 Missing Data 36
2.5 TimeDependent Data 37
2.6 Outlier Analysis 41
2.7 Review Questions and Problems 48
2.8 References for Further Study 51
3 DATA REDUCTION 53
3.1 Dimensions of Large Data Sets 54
3.2 Feature Reduction 56
3.3 Relief Algorithm 66
3.4 Entropy Measure for Ranking Features 68
3.5 PCA 70
3.6 Value Reduction 73
3.7 Feature Discretization: ChiMerge Technique 77
3.8 Case Reduction 80
3.9 Review Questions and Problems 83
3.10 References for Further Study 85
4 LEARNING FROM DATA 87
4.1 Learning Machine 89
4.2 SLT 93
4.3 Types of Learning Methods 99
4.4 Common Learning Tasks 101
4.5 SVMs 105
4.6 kNN: Nearest Neighbor Classifi er 118
4.7 Model Selection versus Generalization 122
4.8 Model Estimation 126
4.9 90% Accuracy: Now What? 132
4.10 Review Questions and Problems 136
4.11 References for Further Study 138
5 STATISTICAL METHODS 140
5.1 Statistical Inference 141
5.2 Assessing Differences in Data Sets 143
5.3 Bayesian Inference 146
5.4 Predictive Regression 149
5.5 ANOVA 155
5.6 Logistic Regression 157
5.7 LogLinear Models 158
5.8 LDA 162
5.9 Review Questions and Problems 164
5.10 References for Further Study 167
6 DECISION TREES AND DECISION RULES 169
6.1 Decision Trees 171
6.2 C4.5 Algorithm: Generating a Decision Tree 173
6.3 Unknown Attribute Values 180
6.4 Pruning Decision Trees 184
6.5 C4.5 Algorithm: Generating Decision Rules 185
6.6 CART Algorithm & Gini Index 189
6.7 Limitations of Decision Trees and Decision Rules 192
6.8 Review Questions and Problems 194
6.9 References for Further Study 198
7 ARTIFICIAL NEURAL NETWORKS 199
7.1 Model of an Artifi cial Neuron 201
7.2 Architectures of ANNs 205
7.3 Learning Process 207
7.4 Learning Tasks Using ANNs 210
7.5 Multilayer Perceptrons (MLPs) 213
7.6 Competitive Networks and Competitive Learning 221
7.7 SOMs 225
7.8 Review Questions and Problems 231
7.9 References for Further Study 233
8 ENSEMBLE LEARNING 235
8.1 EnsembleLearning Methodologies 236
8.2 Combination Schemes for Multiple Learners 240
8.3 Bagging and Boosting 241
8.4 AdaBoost 243
8.5 Review Questions and Problems 245
8.6 References for Further Study 247
9 CLUSTER ANALYSIS 249
9.1 Clustering Concepts 250
9.2 Similarity Measures 253
9.3 Agglomerative Hierarchical Clustering 259
9.4 Partitional Clustering 263
9.5 Incremental Clustering 266
9.6 DBSCAN Algorithm 270
9.7 BIRCH Algorithm 272
9.8 Clustering Validation 275
9.9 Review Questions and Problems 275
9.10 References for Further Study 279
10 ASSOCIATION RULES 280
10.1 MarketBasket Analysis 281
10.2 Algorithm Apriori 283
10.3 From Frequent Itemsets to Association Rules 285
10.4 Improving the Effi ciency of the Apriori Algorithm 286
10.5 FP Growth Method 288
10.6 AssociativeClassifi cation Method 290
10.7 Multidimensional Association–Rules Mining 293
10.8 Review Questions and Problems 295
10.9 References for Further Study 298
11 WEB MINING AND TEXT MINING 300
11.1 Web Mining 300
11.2 Web Content, Structure, and Usage Mining 302
11.3 HITS and LOGSOM Algorithms 305
11.4 Mining Path–Traversal Patterns 310
11.5 PageRank Algorithm 313
11.6 Text Mining 316
11.7 Latent Semantic Analysis (LSA) 320
11.8 Review Questions and Problems 324
11.9 References for Further Study 326
12 ADVANCES IN DATA MINING 328
12.1 Graph Mining 329
12.2 Temporal Data Mining 343
12.3 Spatial Data Mining (SDM) 357
12.4 Distributed Data Mining (DDM) 360
12.5 Correlation Does Not Imply Causality 369
12.6 Privacy, Security, and Legal Aspects of Data Mining 376
12.7 Review Questions and Problems 381
12.8 References for Further Study 382
13 GENETIC ALGORITHMS 385
13.1 Fundamentals of GAs 386
13.2 Optimization Using GAs 388
13.3 A Simple Illustration of a GA 394
13.4 Schemata 399
13.5 TSP 402
13.6 Machine Learning Using GAs 404
13.7 GAs for Clustering 409
13.8 Review Questions and Problems 411
13.9 References for Further Study 413
14 FUZZY SETS AND FUZZY LOGIC 414
14.1 Fuzzy Sets 415
14.2 FuzzySet Operations 420
14.3 Extension Principle and Fuzzy Relations 425
14.4 Fuzzy Logic and Fuzzy Inference Systems 429
14.5 Multifactorial Evaluation 433
14.6 Extracting Fuzzy Models from Data 436
14.7 Data Mining and Fuzzy Sets 441
14.8 Review Questions and Problems 443
14.9 References for Further Study 445
15 VISUALIZATION METHODS 447
15.1 Perception and Visualization 448
15.2 Scientifi c Visualization and
Information Visualization 449
15.3 Parallel Coordinates 455
15.4 Radial Visualization 458
15.5 Visualization Using SelfOrganizing Maps (SOMs) 460
15.6 Visualization Systems for Data Mining 462
15.7 Review Questions and Problems 467
15.8 References for Further Study 468
Appendix A 470
A.1 DataMining Journals 470
A.2 DataMining Conferences 473
A.3 DataMining Forums/Blogs 477
A.4 Data Sets 478
A.5 Comercially and Publicly Available Tools 480
A.6 Web Site Links 489
Appendix B: DataMining Applications 496
B.1 Data Mining for Financial Data Analysis 496
B.2 Data Mining for the Telecomunications Industry 499
B.3 Data Mining for the Retail Industry 501
B.4 Data Mining in Health Care and Biomedical Research 503
B.5 Data Mining in Science and Engineering 506
B.6 Pitfalls of Data Mining 509
Bibliography 510
Index 529
 Support Vector Machines (SVM) – one of the new methodologies, developed based on statistical learning theory, showed large potential for applications in predictive data mining
 Kohonen Maps (Selforganizing Maps –SOM) – one of very applicative neural networks based methodologies for descriptive data mining and multidimensional data visualizations
 DBSCAN clustering algorithm – as a representative of an important class of densitybased clustering methodologies
 Temporal and Spatial Data Mining – including streaming data analyses is an important trend in data mining recognizing value of time and space information in real world applications
 Web and Text Mining
 Parallel and Distributed Data Mining
 Updates on the older techniques presented in the first edition
 Discusses data mining principles and describes representative stateoftheart methods and algorithms originating from different disciplines such as statistics, data bases, pattern recognition, machine learning, neural networks, fuzzy logic, and evolutionary computation
 Detailed algorithms are given with necessary explanations, illustrative examples, and questions and exercises for practice at the end of each chapter
Data Mining methodologies have been evolving over time. New edition includes following new techniques/methodologies
 Support Vector Machines (SVM) – one of the new methodologies, developed based on statistical learning theory, showed large potential for applications in predictive data mining
 Kohonen Maps (Selforganizing Maps –SOM) – one of very applicative neural networks based methodologies for descriptive data mining and multidimensional data visualizations
 DBSCAN clustering algorithm – as a representative of an important class of densitybased clustering methodologies
 Temporal and Spatial Data Mining – including streaming data analyses is an important trend in data mining recognizing value of time and space information in real world applications
 Web and Text Mining
 Parallel and Distributed Data Mining
 Updates on the older techniques presented in the first edition
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