04132nam 22007095 450 991029898010332120200630075607.01-4471-6407-510.1007/978-1-4471-6407-4(CKB)3710000000094950(EBL)1697679(OCoLC)881165892(SSID)ssj0001186546(PQKBManifestationID)11626287(PQKBTitleCode)TC0001186546(PQKBWorkID)11218035(PQKB)10753141(MiAaPQ)EBC1697679(DE-He213)978-1-4471-6407-4(PPN)177825111(EXLCZ)99371000000009495020140327d2014 u| 0engur|n|---|||||txtccrMathematical Tools for Data Mining Set Theory, Partial Orders, Combinatorics /by Dan A. Simovici, Chabane Djeraba2nd ed. 2014.London :Springer London :Imprint: Springer,2014.1 online resource (834 p.)Advanced Information and Knowledge Processing,1610-3947Description based upon print version of record.1-4471-6406-7 Includes bibliographical references at the end of each chapters and index.Sets, Relations and Functions -- Partially Ordered Sets -- Combinatorics -- Topologies and Measures -- Linear Spaces -- Norms and Inner Products -- Spectral Properties of Matrices -- Metric Spaces Topologies and Measures -- Convex Sets and Convex Functions -- Graphs and Matrices -- Lattices and Boolean Algebras -- Applications to Databases and Data Mining -- Frequent Item Sets and Association Rules -- Special Metrics -- Dimensions of Metric Spaces -- Clustering.Data mining essentially relies on several mathematical disciplines, many of which are presented in this second edition of this book.  Topics include partially ordered sets, combinatorics,  general topology, metric spaces, linear spaces, graph theory.  To motivate the reader a significant number of applications of these mathematical tools are included ranging from association rules, clustering algorithms, classification, data constraints, logical data analysis, etc.  The book is intended as a reference for researchers and graduate students.  The current edition is a significant expansion of the first edition.  We strived to make the book self-contained, and only a general knowledge of mathematics is required.  More than 700 exercises are included and they form an integral part of the material.  Many exercises are in reality supplemental material and their solutions are included.Advanced Information and Knowledge Processing,1610-3947Data miningComputer science—MathematicsComputer mathematicsData Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Mathematics of Computinghttps://scigraph.springernature.com/ontologies/product-market-codes/I17001Discrete Mathematics in Computer Sciencehttps://scigraph.springernature.com/ontologies/product-market-codes/I17028Computational Mathematics and Numerical Analysishttps://scigraph.springernature.com/ontologies/product-market-codes/M1400XData mining.Computer science—Mathematics.Computer mathematics.Data Mining and Knowledge Discovery.Mathematics of Computing.Discrete Mathematics in Computer Science.Computational Mathematics and Numerical Analysis.006.312Simovici Dan Aauthttp://id.loc.gov/vocabulary/relators/aut771375Djeraba Chabaneauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910298980103321Mathematical Tools for Data Mining2281727UNINA