I thought readers of sci.stat.edu might be interested in this book. For
more information please visit http://mitpress.mit.edu/026208290X
Principles of Data Mining
David J. Hand, Heikki Mannila, and Padhraic Smyth
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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