Mathematical Methods for Knowledge Discovery and Data Mining (Premier Reference Source)

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Giovanni Felici (Author, Editor), Carlo Vercellis (Editor)

Product Description

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.

About the Author

Giovanni Felici graduated in statistics at the University of Rome ?La Sapienza.? He received his MSc in operations research and operations management at the University of Lancaster, UK, in 1990, and his PhD in operations research at the University of Rome ?La Sapienza.? He is presently a permanent researcher at IASI, the Istituto di Analisi dei Sistemi ed Informatica of the National Research Council (CNR), Italy, where he started his research activity in 1994 working on research projects in logic programming and mathematical optimization. His current research activity is mainly devoted to to the application of optimization techniques to data-mining problems, with particular focus on integer programming algorithms for learning in logic and expert systems.

Carlo Vercellis is full professor at the Politecnico di Milano, where he teaches courses in optimization and business intelligence. He is also director of the research group MOLD?mathematical modeling, optimization, learning from data. He has coordinated national and international research programs funded by EEC, CNR, and MIUR. His current research interests include mathematical models for learning, such as support vector machines and classification trees; data mining and machine learning, and their applications to relational marketing and biolife sciences; optimization models and methods, in particular with applications to supply chain and revenue management. --This text refers to the Digital edition.

Product Details

* Hardcover: 371 pages

* Publisher: Idea Group Reference (October 4, 2007)

* Language: English

* ISBN-10: 1599045281

* ISBN-13: 978-1599045283

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