Principles of Data Mining

David J. Hand

ISBN 10: 026208290X / ISBN 13: 9780262082907

Published by MIT Press Ltd, 2001

About this Item

2001. Hardcover. The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. Series: Adaptive Computation and Machine Learning Series. Num Pages: 578 pages, 89 b&w illus. BIC Classification: UMB; UN; UYA. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 229 x 203 x 25. Weight in Grams: 1180. . . . . . Seller Inventory # V9780262082907

Bibliographic Details

Title: Principles of Data Mining

Publisher: MIT Press Ltd

Publication Date: 2001

Binding: Hardcover

Condition: New

About this title

Synopsis:

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.

About the Author:

David Hand is Professor of Statistics, Department of Mathematics, Imperial College, London.