Practical Applications of Data Mining emphasizes both theory and applications of data mining algorithms. Various topics of data mining techniques are identified and described throughout, including clustering, association rules, rough set theory, probability theory, neural networks, classification, and fuzzy logic. Each of these techniques is explored with a theoretical introduction and its effectiveness is demonstrated with various chapter examples. This book will help any database and IT professional understand how to apply data mining techniques to real-world problems.

　　Following an introduction to data mining principles, Practical Applications of Data Mining introduces association rules to describe the generation of rules as the first step in data mining. It covers classification and clustering methods to show how data can be classified to retrieve information from data. Statistical functions and rough set theory are discussed to demonstrate how statistical and rough set formulas can be used for data analytics and knowledge discovery. Neural networks is an important branch in computational intelligence. It is introduced and explored in the text to investigate the role of neural network algorithms in data analytics.

Preface
Foreword

Ch1: Introduction to Data Mining
Ch2: Association Rules
Ch3: Classification Learning
Ch4: Statistics for Data Mining
Ch5: Rough Sets and Bayes Theories
Ch6: Neural Networks
Ch7: Clustering
Ch8: Fuzzy Information Retrieval