000			01656nam a2200169 4500
999			_c32303 _d32303
020			_a978-0-262-08290-7
040			_aBC-EPAU
041			_aeng
100			_aHAND, David
245			_aPrinciples of data mining
260			_aU.S.A _bMassachusetts institute of technology _c2001
300			_a546 p. _bIll. fig. _c24 cm
700			_aMANNILA, Heikki; SMYTH, Padhraic
942			_c01 _t0418 _u4.3.3
994			_a04180003
520			_aThe 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.