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020 _a9783540332930
_99783540332930
024 7 _a10.1007/11733492
_2doi
035 _avtls000348665
039 9 _a201509030745
_bVLOAD
_c201404121137
_dVLOAD
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040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA76.9.D3
100 1 _aBonchi, Francesco.
_eeditor.
_9331034
245 1 0 _aKnowledge Discovery in Inductive Databases :
_b4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers /
_cedited by Francesco Bonchi, Jean-François Boulicaut.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2006.
300 _aviii, 251 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 ;
_v3933
500 _aSpringer eBooks
505 0 _aInvited Papers -- Data Mining in Inductive Databases -- Mining Databases and Data Streams with Query Languages and Rules -- Contributed Papers -- Memory-Aware Frequent k-Itemset Mining -- Constraint-Based Mining of Fault-Tolerant Patterns from Boolean Data -- Experiment Databases: A Novel Methodology for Experimental Research -- Quick Inclusion-Exclusion -- Towards Mining Frequent Queries in Star Schemes -- Inductive Databases in the Relational Model: The Data as the Bridge -- Transaction Databases, Frequent Itemsets, and Their Condensed Representations -- Multi-class Correlated Pattern Mining -- Shaping SQL-Based Frequent Pattern Mining Algorithms -- Exploiting Virtual Patterns for Automatically Pruning the Search Space -- Constraint Based Induction of Multi-objective Regression Trees -- Learning Predictive Clustering Rules.
520 _aThis book constitutes the thoroughly refereed joint postproceedings of the 4th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2005, held in Porto, Portugal in October 2005 in association with ECML/PKDD. The 20 revised full papers presented together with 2 invited papers were carefully selected during two rounds of reviewing and improvement for inclusion in the book. Bringing together the fields of databases, machine learning, and data mining the papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aBoulicaut, Jean-François.
_eeditor.
_9329894
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9783540332923
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/11733492
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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