Practical Applications of DATA MINING. Sang C Suh Texas A&M University Commerce JONES & BARTLETT LEARNING

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1 Practical Applications of DATA MINING Sang C Suh Texas A&M University Commerce r 3 JONES & BARTLETT LEARNING

2 Contents Preface xi Foreword by Murat M.Tanik xvii Foreword by John Kocur xix Chapter 1 Introduction to Data Mining Traditional Database Management Systems Knowledge Discovery in Databases Pre-Processing Data Warehousing Post-Processing Data-Mining Methods Association Rules Classification Learning Statistical Data Mining Rough Sets for Data Mining Neural Networks for Data Mining Clustering for Data Mining Fuzzy Sets for Data Mining Integrated Framework for Intelligent Databases Practical Applications of Data Mining Healthcare Services Banking Supermarket Applications Medical Image Classification Chapter Summary 27

3 vi CONTENTS Chapter 2 Association Rules Introduction Mining of Association Rules in Market Basket Data Apriori Algorithm Apriori-gen( ) Function Apriori Example AprioriTid Algorithm Attribute-Oriented Rule Generalization Concept Hierarchies Basic Strategies for Attribute-Oriented Induction Basic Attribute-Oriented Induction Algorithm Generation of Discrimination Rules through Attribute-Oriented Induction Association Rules in Hypertext Databases Formal Model Algorithms for Generating Composite Association Rules Quantitative Association Rules Mapping of Quantitative Association Rules Problem Decomposition Partitioning of Quantitative Attributes Mining of Compact Rules Semantic Association Relationships Generalization Algorithm Learning Process Learning Algorithm Mining of Tmie-Constrained Association Rules Time-Constrained Association Rules Properties oftime Constraints Potential Applications Chapter Summary Exercises Selected Bibliographic Notes Chapter Bibliography 75 Chapter 3 Classification Learning Introduction Knowledge Representation Classification Rules Decision Trees Separate-and-Conquer Approach Prism Induct REP, IREP, RIPPER 97

4 CONTENTS vii 3.4 Divide-and-Conquer Approach ID C4.5 and C Partial Decision Tree Chapter Summary Exercises Selected Bibliographic Notes Chapter Bibliography 138 Chapter 4 Statistics for Data Mining Introduction House Sales Data Conditional Probability Equality Tests Correlation Coefficient Contingency Table and the %2 Test Linear Regression House Sales Database Revisited Chapter Summary Exercises Selected Bibliographic Notes Chapter Bibliography 178 Chapter 5 Rough Sets and Bayes' Theories Introduction Bayes'Theorem Rough Sets Data Analysis and Representation Reduction of Condition Attributes and Generation of Decision Rules Applications Based on Bayes'and Rough Sets Customer Tendency Analysis Using Bayes'Theory Contact Lens Prescription Using Rough Set Theory Welding Procedure Using Rough-Set Theory Classification ofautomobiles Using Both Bayes' and Rough Set Theory Chapter Summary Exercises Selected Bibliographic Notes Chapter Bibliography 221 Chapter 6 Neural Networks Introduction Neural Computing and Databases 226

5 viii CONTENTS 6.3 Network Classification Unsupervised Learning Models Supervised Learning Models Parameters of the Learning Process Number of Hidden Layers Number of Hidden Nodes Early Stopping Convergence Curve (Back-Propagation Neural Network) Network Structures Neural Net andtraditional Classifiers Knowledge Discovery Normalization 236 in Databases Backpropagation Neural Network (BPNN) Network Architecture Algorithm Example I 242 Model Example II (Retrieval ofdata Using the BPNN Model) Bidirectional Associative Memory (BAM) Model Network Architecture Algorithm Example with Four TrainingVectors Learning Vector Quantization (LVQ) Model Network Architecture Algorithm Example Probabilistic Neural Network (PNN) Model Network Architecture Algorithm Example Parameter Adjustment Using a Smoothing Factor Chapter Summary Exercises Selected Bibliographic Notes Chapter Bibliography 275 Chapter 7 Clustering Introduction Definition of Clusters and Clustering Clustering Procedures Clustering Concepts Choosing Variables Similarity and Dissimilarity Measurement 285

6 CONTENTS ix Standardization of Variables Weights and Threshold Values Association Rules Clustering Algorithms Hierarchical Algorithms Graph Theory Algorithm with the Single-link Method Partition Algorithms: K"-means Algorithm Density-Search Algorithms Association Rule Algorithms Chapter Summary Exercises Selected Bibliographic Notes Chapter Bibliography 335 Chapter 8 Fuzzy Information Retrieval Introduction Fuzzy Set Basics Fuzzy Set Applications Project Management Data Analysis Nuanced Information Systems Linguistic Variables Fuzzy Query Processing Fuzzy Query Processing Using Fuzzy Tables Convert Raw Data to Fuzzy Member Functions Fuzzy Table Fuzzy Search Engine Fuzzy Table Construction Fuzzy Query Processing Role of Relational Division for Information Retrieval Information Retrieval through Relational Division Information Retrieval through Fuzzy Relational Division Alpha-Cut Thresholds Chapter Summary Exercises Selected Bibliographic Notes Chapter Bibliography 392 Appendix 395 Index 409

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