000 04104nam a22005055i 4500
001 978-3-642-01091-0
003 DE-He213
005 20170628035014.0
007 cr nn 008mamaa
008 100301s2009 gw | s |||| 0|eng d
020 _a9783642010910
_9978-3-642-01091-0
024 7 _a10.1007/978-3-642-01091-0
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
100 1 _aAbraham, Ajith.
_eeditor.
245 1 0 _aFoundations of Computational, IntelligenceVolume 6
_h[electronic resource] :
_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 _aX, 400 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v206
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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aHassanien, Aboul-Ella.
_eeditor.
700 1 _aLeon F. de Carvalho, André Ponce.
_eeditor.
700 1 _aSnášel, Václav.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642010903
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v206
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-01091-0
912 _aZDB-2-ENG
999 _c22531
_d22531