000 03643nam a22005055i 4500
001 978-3-540-31229-1
003 DE-He213
005 20170628034336.0
007 cr nn 008mamaa
008 100805s2006 gw | s |||| 0|eng d
020 _a9783540312291
_9978-3-540-31229-1
024 7 _a10.1007/11539827
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
100 1 _aYoung Lin, Tsau.
_eeditor.
245 1 0 _aFoundations and Novel Approaches in Data Mining
_h[electronic resource] /
_cedited by Tsau Young Lin, Setsuo Ohsuga, Churn-Jung Liau, Xiaohua Hu.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2006.
300 _aX, 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 ;
_v9
505 0 _aFrom the contents Part I: Theoretical Foundations. Commonsense Causal Modeling in the Data Mining Context. Definability of Association Rules in Predicate Calculus. A Measurement-Theoretic Foundation of Rule Interestingness Evaluation. Statistical Independence as Linear Dependence in a Contingency Table. Foundations of Classification -- Part II: Novel Approaches. SVM-OD: SVM Method to Detect Outliers. Extracting Rules from Incomplete Decision Systems: System ERID. Mining for Patterns Based on Contingency Tables by KL-Miner – First Experience. Knowledge Discovery in Fuzzy Databases Using Attribute-Oriented Induction. Rough Set Strategies to Data with Missing Attribute Values. Privacy-Preserving Collaborative Data Mining -- Part III: Novel Applications. Research Issues in Web Structural Delta Mining. Workflow Reduction for Reachable-path Rediscovery in Workflow Mining. Principal Component-based Anomaly Detection Scheme. Making Better Sense of the Demographic Data Value in the Data Mining Procedure.
520 _aData-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor” syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving. Researchers, on the other hand, lack an understanding of the practical issues of data-mining for realworld problems and often concentrate on issues that are of no significance to the practitioners. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining. We have also called on researchers with practical data mining experiences to present new important data-mining topics.
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 _aOhsuga, Setsuo.
_eeditor.
700 1 _aLiau, Churn-Jung.
_eeditor.
700 1 _aHu, Xiaohua.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540283157
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v9
856 4 0 _uhttp://dx.doi.org/10.1007/11539827
912 _aZDB-2-ENG
999 _c19406
_d19406