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Record Nr. |
UNINA9910783721603321 |
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Titolo |
Graph-theoretic techniques for web content mining [[electronic resource] /] / Adam Schenker ... [et al.] |
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Pubbl/distr/stampa |
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[Hackensack], N.J. ; ; London, : World Scientific, 2005 |
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ISBN |
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1-281-37257-9 |
9786611372576 |
981-256-945-6 |
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Descrizione fisica |
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1 online resource (249 p.) |
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Collana |
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Series in machine perception and artificial intelligence ; ; v. 62 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Data mining |
Graph theory - Data processing |
Algorithms |
Multidimensional scaling |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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; Index |
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Sommario/riassunto |
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This 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. |
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