Mining the Web: Discovering Knowledge from Hypertext DataElsevier, 16.10.2002 - 344 Seiten 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 issues—including Web crawling and indexing—Chakrabarti 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 work—painstaking, critical, and forward-looking—readers 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. |
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Mining the Web: Discovering Knowledge from Hypertext Data Soumen Chakrabarti Eingeschränkte Leseprobe - 2002 |
Mining the Web: Discovering Knowledge from Hypertext Data Soumen Chakrabarti Keine Leseprobe verfügbar - 2002 |
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accuracy algorithm applications assigned authority scores Bayesian Bayesian network browser called Chapter class labels Co-citation collection corpus crawl data mining Database degree distribution discussed edge Equation estimate example feature selection fetch focused crawler fraction given Google graph HITS HITS algorithm homepage hub scores hyperlinks hypertext Internet inverted index iterations k-means keyword large number learner linear link-based machine learning mapping Markov blanket matrix maximum entropy measure naive Bayes classifier nodes number of clusters outlinks PageRank parameters parse Pr(c Pr(x precision probabilistic probability problem query terms random ranking recall relevant representation represented response root set sample search engines Section server shown in Figure similarity socket space specific structure subgraph subtree supervised learning tagged techniques term distribution test document textual TFIDF token training data training documents URLs values variable vector vector-space Web graph words Yahoo