Mining the Web : discovering knowledge from hypertext data / Soumen Chakrabarti.

Call Number	005.78/8
Author	Chakrabarti, Soumen.
Title	Mining the Web : discovering knowledge from hypertext data / Soumen Chakrabarti.
Publication	San Francisco, CA : Morgan Kaufmann publishers, ©2003.
Physical Description	1 online resource (xviii, 345 pages) : illustrations
Contents	Crawling the Web -- Web search and information retrieval -- Similarity and clustering -- Supervised learning -- Semisupervised learning -- Social network analysis -- Resource discovery -- The future of Web mining.
Summary	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.</li></ul>.
Subject	DATA MINING. HYPERTEXT SYSTEMS. Web databases. Exploration de données (Informatique) Hypertexte. Bases de données sur le Web. Data mining. Hypertext systems. Web databases. Electronic books.
Multimedia	http://www.sciencedirect.com/science/book/9781558607545

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$a Includes bibliographical references (pages 307-327) and index.

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$a Crawling the Web -- Web search and information retrieval -- Similarity and clustering -- Supervised learning -- Semisupervised learning -- Social network analysis -- Resource discovery -- The future of Web mining.

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Summary	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.</li></ul>.
Contents	Crawling the Web -- Web search and information retrieval -- Similarity and clustering -- Supervised learning -- Semisupervised learning -- Social network analysis -- Resource discovery -- The future of Web mining.
Subject	DATA MINING. HYPERTEXT SYSTEMS. Web databases. Exploration de données (Informatique) Hypertexte. Bases de données sur le Web. Data mining. Hypertext systems. Web databases. Electronic books.
Multimedia	http://www.sciencedirect.com/science/book/9781558607545