LEADER 03870nam 22005775 450 001 9910484827803321 005 20200703072801.0 010 $a3-030-13389-3 024 7 $a10.1007/978-3-030-13389-4 035 $a(CKB)4100000007823523 035 $a(DE-He213)978-3-030-13389-4 035 $a(MiAaPQ)EBC5941381 035 $a(PPN)243769180 035 $a(EXLCZ)994100000007823523 100 $a20190323d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Analysis in Bi-partial Perspective: Clustering and Beyond$b[electronic resource] /$fby Jan W. Owsi?ski 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XIX, 153 p.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v818 311 $a3-030-13388-5 320 $aIncludes bibliographical references and index. 327 $aPreface -- Chapter 1. Notation and main assumptions -- Chapter 2. The problem of cluster analysis -- Chapter 3. The general formulation of the objective function -- Chapter 4. Formulations and rationales for other problems in data analysis, etc. 330 $aThis book presents the bi-partial approach to data analysis, which is both uniquely general and enables the development of techniques for many data analysis problems, including related models and algorithms. It is based on adequate representation of the essential clustering problem: to group together the similar, and to separate the dissimilar. This leads to a general objective function and subsequently to a broad class of concrete implementations. Using this basis, a suboptimising procedure can be developed, together with a variety of implementations. This procedure has a striking affinity with the classical hierarchical merger algorithms, while also incorporating the stopping rule, based on the objective function. The approach resolves the cluster number issue, as the solutions obtained include both the content and the number of clusters. Further, it is demonstrated how the bi-partial principle can be effectively applied to a wide variety of problems in data analysis. The book offers a valuable resource for all data scientists who wish to broaden their perspective on basic approaches and essential problems, and to thus find answers to questions that are often overlooked or have yet to be solved convincingly. It is also intended for graduate students in the computer and data sciences, and will complement their knowledge and skills with fresh insights on problems that are otherwise treated in the standard ?academic? manner. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v818 606 $aEngineering?Data processing 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aData Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11040 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aEngineering?Data processing. 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aData Engineering. 615 24$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a519.53 700 $aOwsi?ski$b Jan W$4aut$4http://id.loc.gov/vocabulary/relators/aut$0913343 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484827803321 996 $aData Analysis in Bi-partial Perspective: Clustering and Beyond$92848004 997 $aUNINA