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Mathematical Methods for Knowledge Discovery and Data Mining
Mathematical Methods for Knowledge Discovery and Data Mining
Date: 16 April 2011, 00:07

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The field of data mining has seen a demand in recent years for the development of ideas and results in an integrated structure. Mathematical Methods for Knowledge Discovery & Data Mining focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; and many others. This Premier Reference Source is an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance and insurance, manufacturing, marketing, performance measurement, and telecommunications.
The importance of knowledge discovery and data mining is evident by the great plethora of books and papers dedicated to this subject. Such methods are finding applications in almost any area of human endeavor. This includes applications in engineering, science, business, medicine, humanities, just to name a few. At the same time, however, there is a great confusion about the development and application of such methods. The main reason for this situation is that many, if not most, of the books examine issues on data mining in a narrow manner. Very few books study issues from the mathematical/algorithmic and also the applications point of view simultaneously. Even fewer books present a comprehensive view of all the critical issues involved with the development and application of such methods to many real-life domains. The present book, edited by two world-renowned scholars, Drs. Giovanni Felici and Carlo Vercellis, is a bright example of the most valuable books in this fast emerging field. The emphasis of this book on the mathematical aspects of knowledge discovery and data-mining methods makes the presentations scientifically sound and easy to understand in depth.

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