000 03810nam a22005655i 4500
001 978-3-540-32408-9
003 DE-He213
005 20161121231023.0
007 cr nn 008mamaa
008 100806s2005 gw | s |||| 0|eng d
020 _a9783540324089
_9978-3-540-32408-9
024 7 _a10.1007/b137220
_2doi
050 4 _aQA75.5-76.95
072 7 _aUY
_2bicssc
072 7 _aUYA
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aCOM031000
_2bisacsh
082 0 4 _a004.0151
_223
245 1 0 _aFoundations of Data Mining and knowledge Discovery
_h[electronic resource] /
_cedited by Tsau Young Lin, Setsuo Ohsuga, Churn-Jung Liau, Xiaohua Hu, Shusaku Tsumoto.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2005.
300 _aXIII, 378 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 ;
_v6
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.
650 0 _aComputer science.
650 0 _aComputers.
650 0 _aArtificial intelligence.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 1 4 _aComputer Science.
650 2 4 _aTheory of Computation.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aYoung Lin, Tsau.
_eeditor.
700 1 _aOhsuga, Setsuo.
_eeditor.
700 1 _aLiau, Churn-Jung.
_eeditor.
700 1 _aHu, Xiaohua.
_eeditor.
700 1 _aTsumoto, Shusaku.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540262572
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v6
856 4 0 _uhttp://dx.doi.org/10.1007/b137220
912 _aZDB-2-ENG
950 _aEngineering (Springer-11647)
999 _c507388
_d507388