000			03103nam a22003855i 4500
001			294826
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007			cr nn 008mamaa
008			150903s2005 gw | o |||| 0|eng d
020			_a9783540318415 _99783540318415
024	7		_a10.1007/b106731 _2doi
035			_avtls000347970
039		9	_a201509030442 _bVLOAD _c201405070458 _dVLOAD _y201402071016 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA76.9.D3
100	1		_aGoethals, Bart. _eeditor. _9313087
245	1	0	_aKnowledge Discovery in Inductive Databases : _bThird International Workshop, KDID 2004, Pisa, Italy, September 20, 2004, Revised Selected and Invited Papers / _cedited by Bart Goethals, Arno Siebes.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2005.
300			_avii, 191 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		_aLecture Notes in Computer Science, _x0302-9743 ; _v3377
500			_aSpringer eBooks
505	0		_aInvited Paper -- Models and Indices for Integrating Unstructured Data with a Relational Database -- Contributed Papers -- Constraint Relaxations for Discovering Unknown Sequential Patterns -- Mining Formal Concepts with a Bounded Number of Exceptions from Transactional Data -- Theoretical Bounds on the Size of Condensed Representations -- Mining Interesting XML-Enabled Association Rules with Templates -- Database Transposition for Constrained (Closed) Pattern Mining -- An Efficient Algorithm for Mining String Databases Under Constraints -- An Automata Approach to Pattern Collections -- Implicit Enumeration of Patterns -- Condensed Representation of EPs and Patterns Quantified by Frequency-Based Measures.
520			_aThis book constitutes the thoroughly refereed joint postproceedings of the Third International Workshop on Knowledge Discovery in Inductive Databases, KDID 2004, held in Pisa, Italy in September 2004 in association with ECML/PKDD. Inductive Databases support data mining and the knowledge discovery process in a natural way. In addition to usual data, an inductive database also contains inductive generalizations, like patterns and models extracted from the data. This book presents nine revised full papers selected from 23 submissions during two rounds of reviewing and improvement together with one invited paper. Various current topics in knowledge discovery and data mining in the framework of inductive databases are addressed.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aSiebes, Arno. _eeditor. _9328750
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783540250821
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/b106731 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c294826 _d294826