Principles of data mining / David Hand, Heikki Mannila, Padhraic Smyth.
By: Hand, D. J [author.]
.
Contributor(s): Mannila, Heikki | Smyth, Padhraic | IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.].
Material type: 
BookSeries: Adaptive computation and machine learning series: Publisher: Cambridge, Massachusetts : MIT Press, 2001Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2001]Description: 1 PDF (xxxii, 546 pages).Content type: text Media type: electronic Carrier type: online resourceISBN: 9780262256308.Subject(s): Data miningGenre/Form: Electronic books.Additional physical formats: Print version: No titleDDC classification: 006.3 Online resources: Abstract with links to resource Also available in print.Summary: 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.
"A Bradford book."
Includes bibliographical references (p. [491]-524) and index.
Restricted to subscribers or individual electronic text purchasers.
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.
Also available in print.
Mode of access: World Wide Web
Description based on PDF viewed 12/28/2015.
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