LEADER 04230nam 22006615 450 001 9910254995403321 005 20251116150158.0 010 $a1-4471-6793-7 024 7 $a10.1007/978-1-4471-6793-8 035 $a(CKB)3710000000620524 035 $a(EBL)4458026 035 $a(SSID)ssj0001654050 035 $a(PQKBManifestationID)16433417 035 $a(PQKBTitleCode)TC0001654050 035 $a(PQKBWorkID)14982285 035 $a(PQKB)10390016 035 $a(DE-He213)978-1-4471-6793-8 035 $a(MiAaPQ)EBC4458026 035 $a(PPN)192770160 035 $a(EXLCZ)993710000000620524 100 $a20160324d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFoundations and methods in combinatorial and statistical data analysis and clustering /$fby Israël César Lerman 205 $a1st ed. 2016. 210 1$aLondon :$cSpringer London :$cImprint: Springer,$d2016. 215 $a1 online resource (664 p.) 225 1 $aAdvanced Information and Knowledge Processing,$x1610-3947 300 $aDescription based upon print version of record. 311 08$a1-4471-6791-0 320 $aIncludes bibliographical references. 327 $aPreface -- On Some Facets of the Partition Set of a Finite Set -- Two Methods of Non-hierarchical Clustering -- Structure and Mathematical Representation of Data -- Ordinal and Metrical Analysis of the Resemblance Notion -- Comparing Attributes by a Probabilistic and Statistical Association I -- Comparing Attributes by a Probabilistic and Statistical Association II -- Comparing Objects or Categories Described by Attributes -- The Notion of ?Natural? Class, Tools for its Interpretation. The Classifiability Concept -- Quality Measures in Clustering -- Building a Classification Tree -- Applying the LLA Method to Real Data -- Conclusion and Thoughts for Future Works. 330 $aThis book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field. With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial and statistical. Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages: Clustering a set of descriptive attributes Clustering a set of objects or a set of object categories Establishing correspondence between these two dual clusterings Tools for interpreting the reasons of a given cluster or clustering are also included. < Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery. 410 0$aAdvanced Information and Knowledge Processing,$x1610-3947 606 $aData mining 606 $aStatistics 606 $aCombinatorial analysis 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 606 $aCombinatorics$3https://scigraph.springernature.com/ontologies/product-market-codes/M29010 615 0$aData mining. 615 0$aStatistics. 615 0$aCombinatorial analysis. 615 14$aData Mining and Knowledge Discovery. 615 24$aStatistics and Computing/Statistics Programs. 615 24$aCombinatorics. 676 $a004 700 $aLerman$b Israe?l Ce?sar$4aut$4http://id.loc.gov/vocabulary/relators/aut$0367840 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254995403321 996 $aFoundations and Methods in Combinatorial and Statistical Data Analysis and Clustering$92135949 997 $aUNINA