02705nam 2200661Ia 450 991078372160332120230617040238.01-281-37257-99786611372576981-256-945-6(CKB)1000000000247225(EBL)259288(OCoLC)475976217(SSID)ssj0000165118(PQKBManifestationID)11180849(PQKBTitleCode)TC0000165118(PQKBWorkID)10125686(PQKB)10622301(MiAaPQ)EBC259288(WSP)00000729 (Au-PeEL)EBL259288(CaPaEBR)ebr10126003(CaONFJC)MIL137257(OCoLC)185300560(EXLCZ)99100000000024722520050816d2005 uy 0engur|n|---|||||txtccrGraph-theoretic techniques for web content mining[electronic resource] /Adam Schenker ... [et al.][Hackensack], N.J. ;London World Scientific20051 online resource (249 p.)Series in machine perception and artificial intelligence ;v. 62Description based upon print version of record.981-256-339-3 Includes bibliographical references and index.Preface; Contents; Chapter 1 Introduction to Web Mining; Chapter 2 Graph Similarity Techniques; Chapter 3 Graph Models for Web Documents; Chapter 4 Graph-Based Clustering; Chapter 5 Graph-Based Classification; Chapter 6 The Graph Hierarchy Construction Algorithm for Web Search Clustering; Chapter 7 Conclusions and Future Work; Appendix A Graph Examples; Appendix B List of Stop Words; Bibliography; IndexThis book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors.Series in machine perception and artificial intelligence ;v. 62.Data miningGraph theoryData processingAlgorithmsMultidimensional scalingData mining.Graph theoryData processing.Algorithms.Multidimensional scaling.006.312Schenker Adam1529140MiAaPQMiAaPQMiAaPQBOOK9910783721603321Graph-theoretic techniques for web content mining3773188UNINA