Daniel T. Larose, Series Editor The field of data mining is growing rapidly. Many people from a wide variety of backgrounds would like to learn about data mining. Thus, there is a need for a set of reference/texts whose aim is to provide lucid, hands-on demonstrations of the growing variety of data mining methods and applications. Our vision for the Wiley Series on Methods and Applications in Data Mining is to publish books that focus on actually doingdata mining, in addition to research and theory.
The list of topics includes (but is not limited to) the following areas:
Interested authors should submit proposals to the Series Editor
Daniel T. Larose, Series Editor, received his PhD in statistics from the University of Connecticut. A professor of statistics and data mining at Central Connecticut State University, he developed and directs Data Mining@CCSU, the world's first online master of science program in data mining. He also provides data mining consulting and statistical consulting.
Please view http://www.wiley.com/WileyCDA/Section/id-301841.html for proposal guidelines
For further information, please contact Paul Petralia, Senior Editor, John Wiley & Sons • firstname.lastname@example.org
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Roger Bilisoly • ISBN 978-0-470-17643-6 • August 2008 • $89.95
This book establishes the methods and algorithms, but more importantly provides the reader with the means to actually perform text mining tasks using readily accessible open source tools.
Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage
Zdravko Markov and Daniel Larose • ISBN 978-0-471-66655-4 • April 2007 • 218 pages • $74.50
This book introduces the reader to methods of data mining on the web, including uncovering patterns in web content (classification, clustering, language processing), structure (graphs, hubs, metrics), and usage (modeling, sequence analysis, performance).
Data Mining Methods and Models
Daniel Larose • ISBN 978-0-471-66656-1 • January 2006 • 344 pages • $101.50 • Wiley-IEEE Press
With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field.
Discovering Knowledge in Data: An Introduction to Data Mining
Daniel Larose • ISBN 978-0-471-66657-8 • November 2004 • 240 pages • $87.95
This book provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.
Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data
Darius Dziuda • ISBN 978-0-470-16373-3 • Fall 2009
This book focuses on practical methods for carrying out data mining in genomics and proteomics and shows the diverse user of these methods how to maximize the chance of extracting new and useful biomedical knowledge from available data.