LEADER 04309nam 22006375 450 001 9910254356903321 005 20200701155044.0 010 $a3-319-45763-2 024 7 $a10.1007/978-3-319-45763-5 035 $a(CKB)3710000001006438 035 $a(DE-He213)978-3-319-45763-5 035 $a(MiAaPQ)EBC6286135 035 $a(MiAaPQ)EBC5610336 035 $a(Au-PeEL)EBL5610336 035 $a(OCoLC)962732092 035 $a(PPN)19713758X 035 $a(EXLCZ)993710000001006438 100 $a20161103d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Knowledge Discovery and Management $eVolume 6 /$fedited by Fabrice Guillet, Bruno Pinaud, Gilles Venturini 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XXI, 278 p. 81 illus., 61 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v665 300 $aIncludes index. 311 $a3-319-45762-4 327 $aPart I: Online learning of a weighted selective naive Bayes classifier with non-convex optimization -- On making skyline queries resistant to outliers -- Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures -- Exact and Approximate Minimal Pattern Mining -- Part II: Comparison of proximity measures for a topological discrimination -- Comparison of linear modularization criteria using the relational formalism, an approach to easily identify resolution limit -- A novel approach to feature selection based on quality estimation metrics -- Ultrametricity of Dissimilarity Spaces and Its Significance for Data Mining -- Part III: SMERA: Semantic Mixed Approach for Web Query Expansion and Reformulation -- Multi-layer ontologies for integrated 3D shape segmentation and annotation -- Ontology Alignment Using Web Linked Ontologies as Background Knowledge -- LIAISON: reconciLIAtion of Individuals profiles across SOcial Networks -- Clustering of Links and Clustering of Nodes: Fusion of Knowledge in Social Networks. 330 $aThis book presents a collection of representative and novel work in the field of data mining, knowledge discovery, clustering and classification, based on expanded and reworked versions of a selection of the best papers originally presented in French at the EGC 2014 and EGC 2015 conferences held in Rennes (France) in January 2014 and Luxembourg in January 2015. The book is in three parts: The first four chapters discuss optimization considerations in data mining. The second part explores specific quality measures, dissimilarities and ultrametrics. The final chapters focus on semantics, ontologies and social networks. Written for PhD and MSc students, as well as researchers working in the field, it addresses both theoretical and practical aspects of knowledge discovery and management. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v665 606 $aData mining 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aData mining. 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aData Mining and Knowledge Discovery. 615 24$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a658.4038 702 $aGuillet$b Fabrice$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPinaud$b Bruno$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVenturini$b Gilles$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254356903321 996 $aAdvances in Knowledge Discovery and Management$91540610 997 $aUNINA