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DATA MINING Concepts, Models, Methods, and Algorithms, SECOND EDITION; CONTENTS; PREFACE TO THE SECOND EDITION; PREFACE TO THE FIRST EDITION; 1: DATA-MINING CONCEPTS; 1.1 INTRODUCTION; 1.2 DATA-MINING ROOTS; 1.3 DATA-MINING PROCESS; 1.4 LARGE DATA SETS; 1.5 DATA WAREHOUSES FOR DATA MINING; 1.6 BUSINESS ASPECTS OF DATA MINING: WHY A DATA-MINING PROJECT FAILS; 1.7 ORGANIZATION OF THIS BOOK; 1.8 REVIEW QUESTIONS AND PROBLEMS; 1.9 REFERENCES FOR FURTHER STUDY; 2: PREPARING THE DATA; 2.1 REPRESENTATION OF RAW DATA; 2.2 CHARACTERISTICS OF RAW DATA; 2.3 TRANSFORMATION OF RAW DATA; 2.4 MISSING DATA.

2.5 TIME-DEPENDENT DATA2.6 OUTLIER ANALYSIS; 2.7 REVIEW QUESTIONS AND PROBLEMS; 2.8 REFERENCES FOR FURTHER STUDY; 3: DATA REDUCTION; 3.1 DIMENSIONS OF LARGE DATA SETS; 3.2 FEATURE REDUCTION; 3.3 RELIEF ALGORITHM; 3.4 ENTROPY MEASURE FOR RANKING FEATURES; 3.5 PCA; 3.6 VALUE REDUCTION; 3.7 FEATURE DISCRETIZATION: CHIMERGE TECHNIQUE; 3.8 CASE REDUCTION; 3.9 REVIEW QUESTIONS AND PROBLEMS; 3.10 REFERENCES FOR FURTHER STUDY; 4: LEARNING FROM DATA; 4.1 LEARNING MACHINE; 4.2 SLT; 4.3 TYPES OF LEARNING METHODS; 4.4 COMMON LEARNING TASKS; 4.5 SVMs; 4.6 KNN : NEAREST NEIGHBOR CLASSIFIER.

4.7 MODEL SELECTION VERSUS GENERALIZATION4.8 MODEL ESTIMATION; 4.9 90% ACCURACY: NOW WHAT?; 4.10 REVIEW QUESTIONS AND PROBLEMS; 4.11 REFERENCES FOR FURTHER STUDY; 5: STATISTICAL METHODS; 5.1 STATISTICAL INFERENCE; 5.2 ASSESSING DIFFERENCES IN DATA SETS; 5.3 BAYESIAN INFERENCE; 5.4 PREDICTIVE REGRESSION; 5.5 ANOVA; 5.6 LOGISTIC REGRESSION; 5.7 LOG-LINEAR MODELS; 5.8 LDA; 5.9 REVIEW QUESTIONS AND PROBLEMS; 5.10 REFERENCES FOR FURTHER STUDY; 6: DECISION TREES AND DECISION RULES; 6.1 DECISION TREES; 6.2 C4.5 ALGORITHM: GENERATING A DECISION TREE; 6.3 UNKNOWN ATTRIBUTE VALUES.

6.4 PRUNING DECISION TREES6.5 C4.5 ALGORITHM: GENERATING DECISION RULES; 6.6 CART ALGORITHM & GINI INDEX; 6.7 LIMITATIONS OF DECISION TREES AND DECISION RULES; 6.8 REVIEW QUESTIONS AND PROBLEMS; 6.9 REFERENCES FOR FURTHER STUDY; 7: ARTIFICIAL NEURAL NETWORKS; 7.1 MODEL OF AN ARTIFICIAL NEURON; 7.2 ARCHITECTURES OF ANNS; 7.3 LEARNING PROCESS; 7.4 LEARNING TASKS USING ANNS; 7.5 MULTILAYER PERCEPTRONS (MLPs); 7.6 COMPETITIVE NETWORKS AND COMPETITIVE LEARNING; 7.7 SOMs; 7.8 REVIEW QUESTIONS AND PROBLEMS; 7.9 REFERENCES FOR FURTHER STUDY; 8: ENSEMBLE LEARNING; 8.1 ENSEMBLE-LEARNING METHODOLOGIES.

8.2 COMBINATION SCHEMES FOR MULTIPLE LEARNERS8.3 BAGGING AND BOOSTING; 8.4 ADABOOST; 8.5 REVIEW QUESTIONS AND PROBLEMS; 8.6 REFERENCES FOR FURTHER STUDY; 9: CLUSTER ANALYSIS; 9.1 CLUSTERING CONCEPTS; 9.2 SIMILARITY MEASURES; 9.3 AGGLOMERATIVE HIERARCHICAL CLUSTERING; 9.4 PARTITIONAL CLUSTERING; 9.5 INCREMENTAL CLUSTERING; 9.6 DBSCAN ALGORITHM; 9.7 BIRCH ALGORITHM; 9.8 CLUSTERING VALIDATION; 9.9 REVIEW QUESTIONS AND PROBLEMS; 9.10 REFERENCES FOR FURTHER STUDY; 10: ASSOCIATION RULES; 10.1 MARKET-BASKET ANALYSIS; 10.2 ALGORITHM APRIORI; 10.3 FROM FREQUENT ITEMSETS TO ASSOCIATION RULES.

10.4 IMPROVING THE EFFICIENCY OF THE APRIORI ALGORITHM.

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 <a href=""http://booksupport.wiley.c.

English.

Includes bibliographical references (p. 510-528) and index.