Data mining concepts

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  • Publicado : 20 de febrero de 2012
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Contents
PREFACE 1 Data Mining Concepts 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 2 Introduction Data-mining roots Data-mining process Large data sets Data warehouses Organization of this book Review questions and problems References for further study xi 1 1 4 5 9 13 16 17 18 19 19 23 24 27 28 33 36 38 39 39 41 46 48 51 54 58 61 62 65 66 71 76 78 vii

Preparing the Data 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8Representation of raw data Characteristics of raw data Transformation of raw data Missing data Time-dependent data Outlier analysis Review questions and problems References for further study

3

Data Reduction 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 Dimensions of large data sets Features reduction Entropy measure for ranking features Principal component analysis Values reduction Featurediscretization: ChiMerge technique Cases reduction Review questions and problems References for further study

4

Learning from Data 4.1 4.2 4.3 4.4 Learning machine Statistical learning theory Types of learning methods Common learning tasks

viii

CONTENTS

4.5 4.6 4.7 5

Model estimation Review questions and problems References for further study

83 87 88 91 91 93 95 98 104 106 107 111 113114 117 117 120 125 129 132 136 137 139 140 142 149 153 154 157 159 161 164 165 165 167 169 170 172 174 176 178 184

Statistical Methods 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 Statistical inference Assessing differences in data sets Bayesian inference Predictive regression Analysis of variance Logistic regression Log-linear models Linear discriminant analysis Review questions and problemsReferences for further study

6

Cluster Analysis 6.1 6.2 6.3 6.4 6.5 6.6 6.7 Clustering concepts Similarity measures Agglomerative hierarchical clustering Partitional clustering Incremental clustering Review questions and problems References for further study

7

Decision Trees and Decision Rules 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 Decision trees C4.5 Algorithm: generating a decision treeUnknown attribute values Pruning decision tree C4.5 Algorithm: generating decision rules Limitations of decision trees and decision rules Associative-classification method Review questions and problems References for further study

8

Association Rules 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 Market-Basket Analysis Algorithm Apriori From frequent itemsets to association rules Improving the efficiencyof the Apriori algorithm Frequent pattern-growth method Multidimensional association-rules mining Web mining HITS and LOGSOM algorithms Mining path-traversal patterns

CONTENTS

ix

8.10 8.11 8.12 9

Text mining Review questions and problems References for further study

187 191 193 195 197 200 201 205 208 214 218 220 221 222 224 229 234 237 239 243 245 247 247 253 257 261 266 268 272274 277 277 278 284 286 289 290 294 295

Artificial Neural Networks 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 Model of an artificial neuron Architectures of artificial neural networks Learning process Learning tasks Multilayer perceptrons Competitive networks and competitive learning Review questions and problems References for further study

10

Genetic Algorithms 10.1 10.2 10.3 10.4 10.5 10.6 10.710.8 Fundamentals of genetic algorithms Optimization using genetic algorithms A simple illustration of a genetic algorithm Schemata Traveling salesman problem Machine learning using genetic algorithms Review questions and problems References for further study

11 Fuzzy Sets and Fuzzy Logic 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 Fuzzy sets Fuzzy set operations Extension principle and fuzzyrelations Fuzzy logic and fuzzy inference systems Multifactorial evaluation Extracting fuzzy models from data Review questions and problems References for further study

12 Visualization Methods 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 Perception and visualization Scientific visualization and information visualization Parallel coordinates Radial visualization Kohonen self-organized maps Visualization...
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