Publications

Principles of Data Mining
David J. Hand, Heikki Mannila, and Padhraic Smyth
For more information please visit http://mitpress.mit.edu/026208290X

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.

David J. Hand is Professor of Statistics, Department of Mathematics,
Imperial College, London. Heikki Mannila is Research Fellow at Nokia
Research Center and Professor, Department of Computer Science and
Engineering, Helsinki University of Technology. Padhraic Smyth is Associate
Professor, Department of Information and Computer Science, the University
of California, Irvine.

8 x 9, 425 pp.
cloth ISBN 0-262-08290-X
Adaptive Computation and Machine Learning series
A Bradford Book

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