000 | 03810nam a22005655i 4500 | ||
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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 |
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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. |
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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 ; _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 |