000 | 03457nam a22005655i 4500 | ||
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001 | 978-3-540-32393-8 | ||
003 | DE-He213 | ||
005 | 20161121231022.0 | ||
007 | cr nn 008mamaa | ||
008 | 100806s2005 gw | s |||| 0|eng d | ||
020 |
_a9783540323938 _9978-3-540-32393-8 |
||
024 | 7 |
_a10.1007/b104039 _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 and Advances in Data Mining _h[electronic resource] / _cedited by Wesley Chu, Tsau Young Lin. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2005. |
|
300 |
_aX, 342 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 |
||
490 | 1 |
_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v180 |
|
505 | 0 | _aThe Mathematics of Learning -- Logical Regression Analysis: From Mathematical Formulas to Linguistic Rules -- A Feature/Attribute Theory for Association Mining and Constructing the Complete Feature Set -- A New Theoretical Framework for K-means-type Clustering -- Clustering via Decision Tree Construction -- Incremental Mining on Association Rules -- Mining Association Rules from Tabular Data Guided by Maximal Frequent Itemsets -- Sequential Pattern Mining by Pattern-Growth: Principles and Extensions -- Web Page Classification -- Web Mining – Concepts, Applications, and Research Directions -- Privacy-Preserving Data Mining. | |
520 | _aWith the growing use of information technology and the recent advances in web systems, the amount of data available to users has increased exponentially. Thus, there is a critical need to understand the content of the data. As a result, data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. In this carefully edited volume a theoretical foundation as well as important new directions for data-mining research are presented. It brings together a set of well respected data mining theoreticians and researchers with practical data mining experiences. The presented theories will give data mining practitioners a scientific perspective in data mining and thus provide more insight into their problems, and the provided new data mining topics can be expected to stimulate further research in these important directions. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aComputers. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aInformation theory. | |
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). |
650 | 2 | 4 | _aInformation Systems and Communication Service. |
650 | 2 | 4 | _aInformation and Communication, Circuits. |
700 | 1 |
_aChu, Wesley. _eeditor. |
|
700 | 1 |
_aLin, Tsau Young. _eeditor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540250579 |
830 | 0 |
_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v180 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/b104039 |
912 | _aZDB-2-ENG | ||
950 | _aEngineering (Springer-11647) | ||
999 |
_c507374 _d507374 |