000			03598nam a22004215i 4500
001			295159
003			MX-SnUAN
005			20170705134232.0
007			cr nn 008mamaa
008			150903s2005 gw | o |||| 0|eng d
020			_a9783540324089 _99783540324089
024	7		_a10.1007/b137220 _2doi
035			_avtls000348381
039		9	_a201509031114 _bVLOAD _c201405070505 _dVLOAD _y201402071026 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aTA329-348
100	1		_aYoung Lin, Tsau. _eeditor. _9328604
245	1	0	_aFoundations of Data Mining and knowledge Discovery / _cedited by Tsau Young Lin, Setsuo Ohsuga, Churn-Jung Liau, Xiaohua Hu, Shusaku Tsumoto.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2005.
300			_axiii, 375 páginas Also available online. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aStudies in Computational Intelligence, _x1860-949X ; _v6
500			_aSpringer eBooks
505	0		_aFrom the contents: Part I Foundations of Data Mining; Knowledge Discovery as Translation; Mathematical Foundation of Association Rules – Mining Associations by Solving Integral Linear Inequalities; Comparative Study of Sequential Pattern Mining Models; Designing Robust Regression Models; A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases; A Careful Look at the Use of Statistical Methodology in Data Mining; Justification and Hypothesis Selection in Data Mining -- Part II Methods of Data Mining; A Comparative Investigation on Model Selection in Binary Factor Analysis; Extraction of Generalized Rules with Automated Attribute Abstraction; Decision Making Based on Hybrid of Multi-knowledge and Naïve Bayes Classifier; First-Order Logic Based Formalism for Temporal Data Mining; An Alternative Approach to Mining Association Rules -- Part III General Knowledge Discovery; Posting Act Tagging Using Transformation-Based Learning.
520			_aFoundations of Data Mining and Knowledge Discovery contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state-of-the-art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aOhsuga, Setsuo. _eeditor. _9328605
700	1		_aLiau, Churn-Jung. _eeditor. _9328606
700	1		_aHu, Xiaohua. _eeditor. _9328607
700	1		_aTsumoto, Shusaku. _eeditor. _9329162
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783540262572
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/b137220 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c295159 _d295159