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Data Mining: Concepts, Models, Methods, and Algorithms, 2nd Edition

ISBN: 978-1-118-02913-8
520 pages
August 2011, Wiley-IEEE Press
Data Mining: Concepts, Models, Methods, and Algorithms, 2nd Edition (1118029135) cover image
This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.

 

If you are an instructor or professor and would like to obtain instructor’s materials, please visit http://booksupport.wiley.com

If you are an instructor or professor and would like to obtain a solutions manual, please send an email to: pressbooks@ieee.org

 

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Preface to the Second Edition xiii

Preface to the First Edition xv

1 DATA-MINING CONCEPTS 1

1.1 Introduction 1

1.2 Data-Mining Roots 4

1.3 Data-Mining 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 Data-Mining 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 Time-Dependent 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 Log-Linear 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 Ensemble-Learning 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 Market-Basket 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 Associative-Classifi 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 Fuzzy-Set 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 Self-Organizing 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 Data-Mining Journals 470

A.2 Data-Mining Conferences 473

A.3 Data-Mining Forums/Blogs 477

A.4 Data Sets 478

A.5 Comercially and Publicly Available Tools 480

A.6 Web Site Links 489

Appendix B: Data-Mining 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

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MEHMED KANTARDZIC, PhD, is a professor in the Department of Computer Engineering and Computer Science (CECS) in the Speed School of Engineering at the University of Louisville, Director of CECS Graduate Studies, as well as Director of the Data Mining Lab. A member of IEEE, ISCA, and SPIE, Dr. Kantardzic has won awards for several of his papers, has been published in numerous referred journals, and has been an invited presenter at various conferences. He has also been a contributor to numerous books.
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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 (Self-organizing Maps –SOM) – one of very applicative neural networks based methodologies for descriptive data mining and multi-dimensional data visualizations
  • DBSCAN clustering algorithm – as a representative of an important class of density-based 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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  • Discusses data mining principles and describes representative state-of-the-art 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 (Self-organizing Maps –SOM) – one of very applicative neural networks based methodologies for descriptive data mining and multi-dimensional data visualizations
  • DBSCAN clustering algorithm – as a representative of an important class of density-based 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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“I therefore gladly salute the second editing of this lovely and valuable book. Researchers, students as well as industry professionals can find the reasons, means and practice to make use of essential data mining methodologies to help their interests.”  (Zentralblatt MATH, 2012)

 

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