000			01826nam a2200217 4500
008			220115b ||||| |||| 00| 0 eng d
020			_a9780262082907 (hb.)
082			_a006.3 _bHAN
100			_aHand, David. _9398
245			_aPrinciples of data mining
260			_aLondon _bMIT Press _c2001
300			_a546p.,
500			_ahttps://mitpress.mit.edu/books/principles-data-mining
520			_a"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."
650			_aData mining _9399
650			_acomputer science _9400
650			_astatistics _9401
700			_aMannila, Heikki. _9402
700			_aSmyth, Padhraic. _9403
942			_cBK
999			_c7666 _d7666