Principles of Data Mining

David J. Hand, Heikki Mannila, Padhraic Smyth (Professor, University of California, Irvine) , Francis Bach (INRIA - Willow Project-Team)

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Hardback

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English

Bradford Books
17 August 2001

Algorithms & data structures; Databases; Mathematical theory of computation

Series: Adaptive Computation and Machine Learning series

Summary
Details

The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

The growing interest in data mining is motivated by a common problem across disciplines- how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local ""memory-based"" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

By: David J. Hand, Heikki Mannila, Padhraic Smyth (Professor, University of California, Irvine)
Series edited by: Francis Bach (INRIA - Willow Project-Team)
Imprint: Bradford Books
Country of Publication: United States
Dimensions: Height: 229mm, Width: 203mm, Spine: 25mm
Weight: 1.179kg
ISBN: 9780262082907
ISBN 10: 026208290X
Series: Adaptive Computation and Machine Learning series
Pages: 578
Publication Date: 17 August 2001
Recommended Age: From 18 years
Audience: Professional and scholarly , College/higher education , Postgraduate, Research & Scholarly , Undergraduate , A / AS level
Format: Hardback
Publisher's Status: Active