Mining the Web: discovering knowledge from hypertext data
Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issuesincluding Web crawling and indexingChakrabarti examines low-level mac...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Boston
Morgan Kaufmann
2003
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| Schriftenreihe: | Morgan Kaufmann series in data management systems
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781558607545/?ar |
| Zusammenfassung: | Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issuesincluding Web crawling and indexingChakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's workpainstaking, critical, and forward-lookingreaders will gain the theoretical and practical understanding they need to contribute to the Web mining effort. * A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining. * Details the special challenges associated with analyzing unstructured and semi-structured data. * Looks at how classical Information Retrieval techniques have been modified for use with Web data. * Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning. * Analyzes current applications for resource discovery and social network analysis. * An excellent way to introduce students to especially vital applications of data mining and machine learning technology.. |
| Beschreibung: | Includes bibliographical references (pages 307-326) and index. - Print version record |
| Umfang: | 1 Online-Ressource (xviii, 344 Seiten) illustrations |
| ISBN: | 9780080511726 0080511724 0585449996 9780585449999 1281035327 9781281035325 9786611035327 661103532X 9781558607545 |
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| spelling | Chakrabarti, Soumen VerfasserIn aut Mining the Web discovering knowledge from hypertext data Soumen Chakrabarti Discovering knowledge from hypertext data Boston Morgan Kaufmann 2003 1 Online-Ressource (xviii, 344 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Morgan Kaufmann series in data management systems Includes bibliographical references (pages 307-326) and index. - Print version record Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issuesincluding Web crawling and indexingChakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's workpainstaking, critical, and forward-lookingreaders will gain the theoretical and practical understanding they need to contribute to the Web mining effort. * A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining. * Details the special challenges associated with analyzing unstructured and semi-structured data. * Looks at how classical Information Retrieval techniques have been modified for use with Web data. * Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning. * Analyzes current applications for resource discovery and social network analysis. * An excellent way to introduce students to especially vital applications of data mining and machine learning technology.. English. Data mining Hypertext systems Data Mining Exploration de données (Informatique) Hypertexte COMPUTERS ; Data Processing 1558607544 Erscheint auch als Druck-Ausgabe 1558607544 |
| spellingShingle | Chakrabarti, Soumen Mining the Web discovering knowledge from hypertext data Data mining Hypertext systems Data Mining Exploration de données (Informatique) Hypertexte COMPUTERS ; Data Processing |
| title | Mining the Web discovering knowledge from hypertext data |
| title_alt | Discovering knowledge from hypertext data |
| title_auth | Mining the Web discovering knowledge from hypertext data |
| title_exact_search | Mining the Web discovering knowledge from hypertext data |
| title_full | Mining the Web discovering knowledge from hypertext data Soumen Chakrabarti |
| title_fullStr | Mining the Web discovering knowledge from hypertext data Soumen Chakrabarti |
| title_full_unstemmed | Mining the Web discovering knowledge from hypertext data Soumen Chakrabarti |
| title_short | Mining the Web |
| title_sort | mining the web discovering knowledge from hypertext data |
| title_sub | discovering knowledge from hypertext data |
| topic | Data mining Hypertext systems Data Mining Exploration de données (Informatique) Hypertexte COMPUTERS ; Data Processing |
| topic_facet | Data mining Hypertext systems Data Mining Exploration de données (Informatique) Hypertexte COMPUTERS ; Data Processing |
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