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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.
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About the Author
David Hand is Professor of Statistics, Department of Mathematics, Imperial College, London.
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- PublisherMIT Press
- Publication date2001
- ISBN 10 026208290X
- ISBN 13 9780262082907
- BindingHardcover
- LanguageEnglish
- Number of pages578
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Principles of Data Mining (Adaptive Computation and Machine Learning)
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Bradford Books, 2001
ISBN 10: 026208290X
ISBN 13: 9780262082907
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