Data Mining Concept Models Methods and Algorithms 2nd Edition by Mehmed Kantardzic ISBN 0470890452 9780470890455

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Product details:

ISBN 10: 0470890452

ISBN 13: 9780470890455

Author:  Mehmed Kantardzic 

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

 

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

1 Data-Mining Concepts 1

1.1 Introduction 2

1.2 Data-Mining Roots 4

1.3 Data-Mining Process 6

1.4 From Data Collection to Data Preprocessing 10

1.5 Data Warehouses for Data Mining 15

1.6 From Big Data to Data Science 18

1.7 Business Aspects of Data Mining: Why a Data-Mining Project Fails? 22

1.8 Organization of This Book 26

1.9 Review Questions and Problems 28

1.10 References for Further Study 30

2 Preparing the Data 33

2.1 Representation of Raw Data 34

2.2 Characteristics of Raw Data 38

2.3 Transformation of Raw Data 40

2.4 Missing Data 43

2.5 Time-Dependent Data 44

2.6 Outlier Analysis 49

2.7 Review Questions and Problems 56

2.8 References for Further Study 59

3 Data Reduction 61

3.1 Dimensions of Large Data Sets 62

3.2 Features Reduction 64

3.3 Relief Algorithm 75

3.4 Entropy Measure for Ranking Features 77

3.5 Principal Component Analysis 80

3.6 Value Reduction 83

3.7 Feature Discretization: ChiMerge Technique 86

3.8 Case Reduction 90

3.9 Review Questions and Problems 93

3.10 References for Further Study 95

4 Learning from Data 97

4.1 Learning Machine 99

4.2 Statistical Learning Theory 104

4.3 Types of Learning Methods 110

4.4 Common Learning Tasks 112

4.5 Support Vector Machines 117

4.6 Semi-Supervised Support Vector Machines (S3VM) 131

4.7 kNN: Nearest Neighbor Classifier 134

4.8 Model Selection vs. Generalization 138

4.9 Model Estimation 142

4.10 Imbalanced Data Classification 150

4.11 90% Accuracy … Now What? 154

4.12 Review Questions and Problems 158

4.13 References for Further Study 161

5 Statistical Methods 165

5.1 Statistical Inference 166

5.2 Assessing Differences in Data Sets 168

5.3 Bayesian Inference 172

5.4 Predictive Regression 175

5.5 Analysis of Variance 181

5.6 Logistic Regression 184

5.7 Log-Linear Models 185

5.8 Linear Discriminant Analysis 189

5.9 Review Questions and Problems 191

5.10 References for Further Study 194

6 Decision Trees and Decision Rules 197

6.1 Decision Trees 199

6.2 C4.5 Algorithm: Generating a Decision Tree 201

6.3 Unknown Attribute Values 209

6.4 Pruning Decision Trees 214

6.5 C4.5 Algorithm: Generating Decision Rules 215

6.6 Cart Algorithm and Gini Index 219

6.7 Limitations of Decision Trees and Decision Rules 222

6.8 Review Questions and Problems 225

6.9 References for Further Study 229

7 Artificial Neural Networks 231

7.1 Model of an Artificial Neuron 233

7.2 Architectures of Artificial Neural Networks 237

7.3 Learning Process 239

7.4 Learning Tasks Using Anns 243

7.5 Multilayer Perceptrons 245

7.6 Competitive Networks and Competitive Learning 255

7.7 Self-Organizing Maps 259

7.8 Deep Learning 264

7.9 Convolutional Neural Networks (CNNs) 270

7.10 Review Questions and Problems 273

7.11 References for Further Study 276

8 Ensemble Learning 279

8.1 Ensemble Learning Methodologies 280

8.2 Combination Schemes for Multiple Learners 285

8.3 Bagging and Boosting 286

8.4 AdaBoost 288

8.5 Review Questions and Problems 290

8.6 References for Further Study 293

9 Cluster Analysis 295

9.1 Clustering Concepts 296

9.2 Similarity Measures 299

9.3 Agglomerative Hierarchical Clustering 306

9.4 Partitional Clustering 310

9.5 Incremental Clustering 313

9.6 DBSCAN Algorithm 317

9.7 BIRCH Algorithm 320

9.8 Clustering Validation 323

9.9 Review Questions and Problems 328

9.10 References for Further Study 333

10 Association Rules 335

10.1 Market-Basket Analysis 337

10.2 Algorithm Apriori 338

10.3 From Frequent Itemsets to Association Rules 340

10.4 Improving the Efficiency of the Apriori Algorithm 342

10.5 Frequent Pattern Growth Method 344

10.6 Associative-Classification Method 346

10.7 Multidimensional Association Rule Mining 349

10.8 Review Questions and Problems 351

10.9 References for Further Study 355

11 Web Mining and Text Mining 357

11.1 Web Mining 358

11.2 Web Content, Structure, and Usage Mining 360

11.3 Hits and Logsom Algorithms 362

11.4 Mining Path-Traversal Patterns 368

11.5 PageRank Algorithm 371

11.6 Recommender Systems 374

11.7 Text Mining 375

11.8 Latent Semantic Analysis 379

11.9 Review Questions and Problems 385

11.10 References for Further Study 388

12 Advances in Data Mining 391

12.1 Graph Mining 392

12.2 Temporal Data Mining 406

12.3 Spatial Data Mining 422

12.4 Distributed Data Mining 426

12.5 Correlation Does not Imply Causality! 435

12.6 Privacy, Security, and Legal Aspects of Data Mining 442

12.7 Cloud Computing Based on Hadoop and Map/Reduce 449

12.8 Reinforcement Learning 454

12.9 Review Questions and Problems 459

12.10 References for Further Study 461

13 Genetic Algorithms 465

13.1 Fundamentals of Genetic Algorithms 466

13.2 Optimization Using Genetic Algorithms 468

13.3 A Simple Illustration of a Genetic Algorithm 474

13.4 Schemata 480

13.5 Traveling Salesman Problem 483

13.6 Machine Learning Using Genetic Algorithms 485

13.7 Genetic Algorithms for Clustering 490

13.8 Review Questions and Problems 493

13.9 References for Further Study 494

14 Fuzzy Sets and Fuzzy Logic 497

14.1 Fuzzy Sets 498

14.2 Fuzzy Set Operations 504

14.3 Extension Principle and Fuzzy Relations 509

14.4 Fuzzy Logic and Fuzzy Inference Systems 513

14.5 Multifactorial Evaluation 518

14.6 Extracting Fuzzy Models from Data 521

14.7 Data Mining and Fuzzy Sets 526

14.8 Review Questions and Problems 528

14.9 References for Further Study 530

15 Visualization Methods 533

15.1 Perception and Visualization 534

15.2 Scientific Visualization and Information Visualization 535

15.3 Parallel Coordinates 542

15.4 Radial Visualization 544

15.5 Visualization Using Self-Organizing Maps 547

15.6 Visualization Systems for Data Mining 549

15.7 Review Questions and Problems 554

15.8 References for Further Study 555

 

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