000 03197nam a22005175i 4500
001 978-3-642-29807-3
003 DE-He213
005 20140220083317.0
007 cr nn 008mamaa
008 120709s2012 gw | s |||| 0|eng d
020 _a9783642298073
_9978-3-642-29807-3
024 7 _a10.1007/978-3-642-29807-3
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aWu, Junjie.
_eauthor.
245 1 0 _aAdvances in K-means Clustering
_h[electronic resource] :
_bA Data Mining Thinking /
_cby Junjie Wu.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2012.
300 _aXVI, 178 p. 50 illus., 44 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
505 0 _aCluster Analysis and K-means Clustering: An Introduction -- The Uniform Effect of K-means Clustering -- Generalizing Distance Functions for Fuzzy c-Means Clustering -- Information-Theoretic K-means for Text Clustering -- Selecting External Validation Measures for K-means Clustering -- K-means Based Local Decomposition for Rare Class Analysis -- K-means Based Consensus Clustering.
520 _aNearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.
650 0 _aComputer science.
650 0 _aDatabase management.
650 0 _aData mining.
650 0 _aEconomics
_xStatistics.
650 0 _aManagement information systems.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 2 4 _aBusiness Information Systems.
650 2 4 _aDatabase Management.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642298066
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-29807-3
912 _aZDB-2-SCS
999 _c103087
_d103087