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020 _a9783642010910
_99783642010910
024 7 _a10.1007/9783642010910
_2doi
035 _avtls000353019
039 9 _a201509030927
_bVLOAD
_c201405060314
_dVLOAD
_y201402180934
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aTA329-348
100 1 _aAbraham, Ajith.
_eeditor.
_9316139
245 1 0 _aFoundations of Computational, IntelligenceVolume 6 :
_bData Mining /
_cedited by Ajith Abraham, Aboul-Ella Hassanien, André Ponce Leon F. de Carvalho, Václav Snášel.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2009.
300 _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 ;
_v206
500 _aSpringer eBooks
505 0 _aData Click Streams and Temporal Data Mining -- Mining and Analysis of Clickstream Patterns -- An Overview on Mining Data Streams -- Data Stream Mining Using Granularity-Based Approach -- Time Granularity in Temporal Data Mining -- Mining User Preference Model from Utterances -- Text and Rule Mining -- Text Summarization: An Old Challenge and New Approaches -- From Faceted Classification to Knowledge Discovery of Semi-structured Text Records -- Multi-value Association Patterns and Data Mining -- Clustering Time Series Data: An Evolutionary Approach -- Support Vector Clustering: From Local Constraint to Global Stability -- New Algorithms for Generation Decision Trees—Ant-Miner and Its Modifications -- Data Mining Applications -- Automated Incremental Building of Weighted Semantic Web Repository -- A Data Mining Approach for Adaptive Path Planning on Large Road Networks -- Linear Models for Visual Data Mining in Medical Images -- A Framework for Composing Knowledge Discovery Workflows in Grids -- Distributed Data Clustering: A Comparative Analysis.
520 _aFinding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, business, health care, banking, retail, and many others. Advanced representation schemes and computational intelligence techniques such as rough sets, neural networks; decision trees; fuzzy logic; evolutionary algorithms; artificial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are applied to Data Mining problems. Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. It is also observed that strong scientific advances have been made when issues from different research areas are integrated. This Volume comprises of 15 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for Data Mining.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aHassanien, Aboul-Ella.
_eeditor.
_9316384
700 1 _aLeon F. de Carvalho, André Ponce.
_eeditor.
_9335929
700 1 _aSnášel, Václav.
_eeditor.
_9323744
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9783642010903
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-642-01091-0
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c299154
_d299154