000 03454nam a22005535i 4500
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
024 7 _a10.1007/3-540-28349-8
_2doi
050 4 _aQA76.9.D35
072 7 _aUMB
_2bicssc
072 7 _aURY
_2bicssc
072 7 _aCOM031000
_2bisacsh
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.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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.
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