000 | 03643nam a22005055i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/11539827 _2doi |
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050 | 4 | _aTA329-348 | |
050 | 4 | _aTA640-643 | |
072 | 7 |
_aTBJ _2bicssc |
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072 | 7 |
_aMAT003000 _2bisacsh |
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082 | 0 | 4 |
_a519 _223 |
100 | 1 |
_aYoung Lin, Tsau. _eeditor. |
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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. |
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300 |
_aX, 378 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Computational Intelligence, _x1860-949X ; _v9 |
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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 |