Principles of Data Mining
Hand, David J.; Mannila, Heikki; Smyth, Padhraic
Sold by ThriftBooks-Atlanta, AUSTELL, GA, U.S.A.
AbeBooks Seller since March 24, 2009
Used - Hardcover
Condition: Used - Very good
Quantity: 1 available
Add to basketSold by ThriftBooks-Atlanta, AUSTELL, GA, U.S.A.
AbeBooks Seller since March 24, 2009
Condition: Used - Very good
Quantity: 1 available
Add to basketMay have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 2.74.
Seller Inventory # G026208290XI4N00
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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