000 | 03454nam a22005535i 4500 | ||
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001 | 978-3-540-28349-2 | ||
003 | DE-He213 | ||
005 | 20161121230528.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2006 gw | s |||| 0|eng d | ||
020 |
_a9783540283492 _9978-3-540-28349-2 |
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024 | 7 |
_a10.1007/3-540-28349-8 _2doi |
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050 | 4 | _aQA76.9.D35 | |
072 | 7 |
_aUMB _2bicssc |
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072 | 7 |
_aURY _2bicssc |
|
072 | 7 |
_aCOM031000 _2bisacsh |
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082 | 0 | 4 |
_a005.74 _223 |
245 | 1 | 0 |
_aGrouping Multidimensional Data _h[electronic resource] : _bRecent Advances in Clustering / _cedited by Jacob Kogan, Charles Nicholas, Marc Teboulle. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2006. |
|
300 |
_aXII, 268 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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505 | 0 | _aThe Star Clustering Algorithm for Information Organization -- A Survey of Clustering Data Mining Techniques -- Similarity-Based Text Clustering: A Comparative Study -- Clustering Very Large Data Sets with Principal Direction Divisive Partitioning -- Clustering with Entropy-Like k-Means Algorithms -- Sampling Methods for Building Initial Partitions -- TMG: A MATLAB Toolbox for Generating Term-Document Matrices from Text Collections -- Criterion Functions for Clustering on High-Dimensional Data. | |
520 | _aClustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection. Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview. The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aData structures (Computer science). | |
650 | 0 |
_aComputer science _xMathematics. |
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650 | 0 | _aInformation storage and retrieval. | |
650 | 0 | _aPattern recognition. | |
650 | 0 | _aStatistics. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aData Structures, Cryptology and Information Theory. |
650 | 2 | 4 | _aInformation Storage and Retrieval. |
650 | 2 | 4 | _aStatistical Theory and Methods. |
650 | 2 | 4 | _aMath Applications in Computer Science. |
650 | 2 | 4 | _aStatistics and Computing/Statistics Programs. |
650 | 2 | 4 | _aPattern Recognition. |
700 | 1 |
_aKogan, Jacob. _eeditor. |
|
700 | 1 |
_aNicholas, Charles. _eeditor. |
|
700 | 1 |
_aTeboulle, Marc. _eeditor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540283485 |
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/3-540-28349-8 |
912 | _aZDB-2-SCS | ||
950 | _aComputer Science (Springer-11645) | ||
999 |
_c500115 _d500115 |