LEADER 06646nam 22008895 450 001 996465946903316 005 20200704151010.0 010 $a3-540-78137-4 024 7 $a10.1007/978-3-540-78137-0 035 $a(CKB)1000000000490575 035 $a(SSID)ssj0000317696 035 $a(PQKBManifestationID)11248519 035 $a(PQKBTitleCode)TC0000317696 035 $a(PQKBWorkID)10294923 035 $a(PQKB)11374751 035 $a(DE-He213)978-3-540-78137-0 035 $a(MiAaPQ)EBC6281517 035 $a(MiAaPQ)EBC4976222 035 $a(Au-PeEL)EBL4976222 035 $a(CaONFJC)MIL134302 035 $a(OCoLC)1024251880 035 $a(PPN)123743656 035 $a(EXLCZ)991000000000490575 100 $a20100301d2008 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aFormal Concept Analysis$b[electronic resource] $e6th International Conference, ICFCA 2008, Montreal, Canada, February 25-28, 2008, Proceedings /$fedited by Raoul Medina, Sergei Obiedkov 205 $a1st ed. 2008. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2008. 215 $a1 online resource (XII, 328 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v4933 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-78136-6 320 $aIncludes bibliographical references and index. 327 $aCommunicative Rationality, Logic, and Mathematics -- Actionability and Formal Concepts: A Data Mining Perspective -- Acquiring Generalized Domain-Range Restrictions -- A Finite Basis for the Set of -Implications Holding in a Finite Model -- Lexico-Logical Acquisition of OWL DL Axioms -- From Concepts to Concept Lattice: A Border Algorithm for Making Covers Explicit -- A Formal Context for Symmetric Dependencies -- The Number of Plane Diagrams of a Lattice -- Spectral Lattices of -Formal Contexts -- About Keys of Formal Context and Conformal Hypergraph -- An Algebraization of Linear Continuum Structures -- On the Complexity of Computing Generators of Closed Sets -- Generating Positive and Negative Exact Rules Using Formal Concept Analysis: Problems and Solutions -- On the Merge of Factor Canonical Bases -- Lattices of Rough Set Abstractions as P-Products -- Scale Coarsening as Feature Selection -- Formal Concept Analysis for the Identification of Combinatorial Biomarkers in Breast Cancer -- Handling Spatial Relations in Logical Concept Analysis to Explore Geographical Data -- Analysis of Social Communities with Iceberg and Stability-Based Concept Lattices -- Formal Concept Analysis Enhances Fault Localization in Software -- Refactorings of Design Defects Using Relational Concept Analysis -- Contingency Structures and Concept Analysis -- Comparison of Dual Orderings in Time II. 330 $aFormal Concept Analysis (FCA) is a mathematical theory of concepts and c- ceptualhierarchyleadingtomethodsforconceptuallyanalyzingdataandkno- edge. The theoryitselfstronglyreliesonorderandlatticetheory,whichhasbeen studied by mathematicians over decades. FCA proved itself highly relevant in several applications from the beginning, and, over the last years, the range of applicationshaskeptgrowing. The mainreasonfor this comesfromthe fact that our modern society has turned into an ?information? society. After years and years of using computers, companies realized they had stored gigantic amounts of data. Then, they realized that this data, just rough information for them, might become a real treasure if turned into knowledge. FCA is particularly well suited for this purpose. From relational data, FCA can extract implications, - pendencies, concepts and hierarchies of concepts, and thus capture part of the knowledge hidden in the data. The ICFCA conference series gathers researchers from all over the world, being the main forum to present new results in FCA and related ?elds. These results range from theoretical novelties to advances in FCA-related algorithmic issues, as well as application domains of FCA. ICFCA 2008 was in the same vein as its predecessors: high-quality papers and presentations, the place of real debate and exchange of ideas. ICFCA 2008 contributed to strengthening the links between theory and applications. The high quality of the presentations was the result of the remarkable work of the authors and the reviewers. We wish to thank the reviewers for all their valuable comments, which helped the authors to improve their presentations. 410 0$aLecture Notes in Artificial Intelligence ;$v4933 606 $aArtificial intelligence 606 $aComputer science?Mathematics 606 $aMathematical logic 606 $aSoftware engineering 606 $aData mining 606 $aAlgebra 606 $aOrdered algebraic structures 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aDiscrete Mathematics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17028 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 606 $aSoftware Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/I14029 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aOrder, Lattices, Ordered Algebraic Structures$3https://scigraph.springernature.com/ontologies/product-market-codes/M11124 615 0$aArtificial intelligence. 615 0$aComputer science?Mathematics. 615 0$aMathematical logic. 615 0$aSoftware engineering. 615 0$aData mining. 615 0$aAlgebra. 615 0$aOrdered algebraic structures. 615 14$aArtificial Intelligence. 615 24$aDiscrete Mathematics in Computer Science. 615 24$aMathematical Logic and Formal Languages. 615 24$aSoftware Engineering. 615 24$aData Mining and Knowledge Discovery. 615 24$aOrder, Lattices, Ordered Algebraic Structures. 676 $a006.1 702 $aMedina$b Raoul$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aObiedkov$b Sergei$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aInternational Conference on Formal Concept Analysis 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465946903316 996 $aFormal Concept Analysis$9772457 997 $aUNISA