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Book Review: The Handbook of Data Mining by N. Ye

The Handbook of Data Mining is an impressive collection from 28 contributors. The book is an excellent source for those who are looking for data mining publications but don’t want to be limited to a typical length of a conference paper. Authors were given plenty of space to describe the ideas extensively with substantial examples and case studies. The audience that would benefit the most from reading this book will have an advanced background in data mining or statistics. This book would make an excellent text book for an advanced “Topics in Data Mining” course where selected algorithms and methods could be researched based on the included papers and extensive list of references. The book would also be helpful for users of data mining solutions who need to gain a more in-depth knowledge into how selected algorithms work and how to apply these algorithms to solve their problems. Also, developers of data mining solutions will find many useful chapters with essential formulas and pseudo code. However, the algorithms featured in this book may not be discussed at a level that would solve all implementation problems and supplemental materials would be necessary. Papers published in this book are organized into three major sections on methodologies, management of data mining, and applications of data mining. The Methodologies section is geared toward theoretical aspects of algorithms and constitutes the largest section of this book. The section devotes a significant amount of space to several major data mining algorithms for learning classification models, unsupervised learning, data mining for anomaly detection, mining sequential patterns, as well time series. There are several chapters that focus on methodology aspects of data mining tasks. Readers should be aware that papers preINTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION, 18(2), 233–234 Copyright © 2005, Lawrence Erlbaum Associates, Inc.