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010   $a1-281-37257-9
010   $a9786611372576
010   $a981-256-945-6
035   $a(CKB)1000000000247225
035   $a(EBL)259288
035   $a(OCoLC)475976217
035   $a(SSID)ssj0000165118
035   $a(PQKBManifestationID)11180849
035   $a(PQKBTitleCode)TC0000165118
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035   $a(OCoLC)185300560
035   $a(EXLCZ)991000000000247225
100   $a20050816d2005      uy             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 00$aGraph-theoretic techniques for web content mining$b[electronic resource] /$fAdam Schenker ... [et al.]
210   $a[Hackensack], N.J. ;$aLondon $cWorld Scientific$d2005
215   $a1 online resource (249 p.)
225 1 $aSeries in machine perception and artificial intelligence ;$vv. 62
300   $aDescription based upon print version of record.
311   $a981-256-339-3 
320   $aIncludes bibliographical references and index.
327   $aPreface; 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; Index
330   $aThis book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors.
410  0$aSeries in machine perception and artificial intelligence ;$vv. 62.
606   $aData mining
606   $aGraph theory$xData processing
606   $aAlgorithms
606   $aMultidimensional scaling
615  0$aData mining.
615  0$aGraph theory$xData processing.
615  0$aAlgorithms.
615  0$aMultidimensional scaling.
676   $a006.312
701   $aSchenker$b Adam$01529140
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910783721603321
996   $aGraph-theoretic techniques for web content mining$93773188
997   $aUNINA