Table of Contents

PREFACE.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.1.6 Organization of this book.1.7 Review questions and problems.1.8 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 data.2.6 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 Features reduction.3.3 Entropy measure for ranking features.3.4 Principal component analysis.3.5 Values reduction.3.6 Feature discretization: ChiMerge technique.3.7 Cases reduction.3.8 Review questions and problems.3.9 References for further study.4 Learning from Data.4.1 Learning machine.4.2 Statistical learning theory.4.3 Types of learning methods.4.4 Common learning tasks.4.5 Model estimation.4.6 Review questions and problems.4.7 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 Analysis of variance.5.6 Logistic regression.5.7 Log-linear models.5.8 Linear discriminant analysis.5.9 Review questions and problems.5.10 References for further study.6 Cluster Analysis.6.1 Clustering concepts.6.2 Similarity measures.6.3 Agglomerative hierarchical clustering.6.4 Partitional clustering.6.5 Incremental clustering.6.6 Review questions and problems.6.7 References for further study.7 Decision Trees and Decision Rules.7.1 Decision trees.7.2 C4.5 Algorithm: generating a decision tree.7.3 Unknown attribute values.7.4 Pruning decision tree.7.5 C4.5 Algorithm: generating decision rules.7.6 Limitations of decision trees and decision rules.7.7 Associative-classification method.7.8 Review questions and problems.7.9 References for further study.8 Association Rules.8.1 Market-Basket Analysis.8.2 Algorithm Apriori.8.3 From frequent itemsets to association rules.8.4 Improving the efficiency of the Apriori algorithm.8.5 Frequent pattern-growth method.8.6 Multidimensional association-rules mining.8.7 Web mining.8.8 HITS and LOGSOM algorithms.8.9 Mining path-traversal patterns.8.10 Text mining.8.11 Review questions and problems.8.12 References for further study.9 Artificial Neural Networks.9.1 Model of an artificial neuron.9.2 Architectures of artificial neural networks.9.3 Learning process.9.4 Learning tasks.9.5 Multilayer perceptrons.9.6 Competitive networks and competitive learning.9.7 Review questions and problems.9.8 References for further study.10 Genetic Algorithms.10.1 Fundamentals of genetic algorithms.10.2 Optimization using genetic algorithms.10.3 A simple illustration of a genetic algorithm.10.4 Schemata.10.5 Traveling salesman problem.10.6 Machine learning using genetic algorithms.10.7 Review questions and problems.10.8 References for further study.11 Fuzzy Sets and Fuzzy Logic.11.1 Fuzzy sets.11.2 Fuzzy set operations.11.3 Extension principle and fuzzy relations.11.4 Fuzzy logic and fuzzy inference systems.11.5 Multifactorial evaluation.11.6 Extracting fuzzy models from data.11.7 Review questions and problems.11.8 References for further study.12 Visualization Methods.12.1 Perception and visualization.12.2 Scientific visualization and information visualization.12.3 Parallel coordinates.12.4 Radial visualization.12.5 Kohonen self-organized maps.12.6 Visualization systems for data mining.12.7 Review questions and problems.12.8 References for further study.13 References.APPENDIX A: Data-Mining Tools.Al Commercially and publicly available tools.A2 Web site links.APPENDIX B: Data-Mining Applications.Bl Data mining for financial data analysis.B2 Data mining for the telecommunications industry.B3 Data mining for the retail industry.B4 Data mining in healthcare and biomedical research.B5 Data mining in science and engineering.B6 Pitfalls of data mining.INDEX.ABOUT THE AUTHOR.

About the Author

MEHMED KANTARDZIC, PhD, is an associate professor in the Department of Computer Engineering and Computer Science (CECS) at the University of Louisville, and is Director of the Data Mining Lab. He was a visiting faculty member in the CECS from 1995 until August 2001. Dr. Kantardzic earned his PhD in 1980 at the University of Sarajevo, Bosnia, where he was an associate professor and Head of the Laboratory for Electronics and Computer Science until 1994.

Reviews

"...a very readable and up-to-date introduction to data mining..." (Quality & Reliability Engineering International, Vol. 21 (4) June 2005) "...suitable for a graduate level course in data mining...I enjoyed reading this book and recommend it highly." (Journal of Statistical Computation & Simulation, April 2004) "...clear and well understandable...recommended as basic guidance...practitioners will profit from the author's long experience..." (Zentralblatt Math, Vol. 1027, 2004) "...reviews state-of-the-art techniques for analyzing enormous quantities of raw data..." (Quarterly of Applied Mathematics, Vol. LXI, No. 3, September 2003) "...a valuable book... I truly enjoyed reading the book and I am glad to recommend it to anyone working in this fascinating field." (IIE Transactions) "...detailed, well illustrated, and easy to understand...comprehensive...a good book..." (Mathematical Reviews 2003h) "...this is probably the first data-mining book that I would select from my bookshelf as reading material for a statistician..." (Technometrics, Vol. 45, No. 3, August 2003)