Foreword

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Data mining and knowledge discovery are a rapidly changing field. Research in data mining has focused on several qualitative and quantitative factors. The current book contains a compilation of chapters ranging from several different areas relating to research in data mining. The book chapters cover the four major areas of classification, forecasting, mathematical modeling and data visualization. Additionally, some of the chapters are focused on the application of data mining for predicting fraud and other qualitative issues. The reader of the book will find broad coverage of several data mining techniques, including Bayesian techniques, forecasting, association rules, genetic algorithms, neural networks, linear discriminant analysis, data preparation and data warehousing issues.

All the chapters that appear in this book were refereed by at least two reviewers (including the editor). I was very fortunate to receive a large number of papers from many active researchers, educators and practitioners from all over the world. In several ways, the book provides a global representation of the state of research in the data mining field.

Putting together a book of this level requires a lot of effort and help from colleagues. I was very fortunate that all the reviewers and the authors met the review and submission deadlines. I would like to personally extend my special thanks to the authors and the reviewers for their excellent contributions and quick turnaround times. Finally, I would like to thank my graduate assistant and Idea Group Publishing staff for their administrative and moral support for this project.

Parag C. Pendharkar
July 2002

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