Principles of Data Mining (Adaptive Computation and Machine Learning)

Hand, David J., Mannila, Heikki

ISBN 10: 026208290X / ISBN 13: 9780262082907

Published by Bradford Books, 2001

About this Item

Ausreichend/Acceptable: Exemplar mit vollständigem Text und sämtlichen Abbildungen oder Karten. Schmutztitel oder Vorsatz können fehlen. Einband bzw. Schutzumschlag weisen unter Umständen starke Gebrauchsspuren auf. / Describes a book or dust jacket that has the complete text pages (including those with maps or plates) but may lack endpapers, half-title, etc. (which must be noted). Binding, dust jacket (if any), etc may also be worn. Seller Inventory # M0026208290X-B

Bibliographic Details

Title: Principles of Data Mining (Adaptive ...

Publisher: Bradford Books

Publication Date: 2001

Binding: Hardcover

Condition: acceptable

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.