Principles of Data Mining (Adaptive Computation and Machine Learning series)

David J. Hand, Francis Bach

ISBN 10: 026208290X / ISBN 13: 9780262082907

Published by MIT Press 10/10/2001, 2001

About this Item

This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .

Seller Inventory # 7719-9780262082907

Bibliographic Details

• Title: Principles of Data Mining (Adaptive Computation and Machine Learning series)
• Publisher: MIT Press 10/10/2001
• Publication Date: 2001
• Language: English
• ISBN 10: 026208290X
• ISBN 13: 9780262082907
• Binding: Hardcover
• Condition: Very Good

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.