Mining the Web: Discovering Knowledge from Hypertext Data
Soumen Chakrabarti, Soumen (Asst. Prof. of Computer Science Chakrabarti, Indian Institute of Technology Bombay)
Morgan Kaufmann, 23.10.2002 - 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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MDS map of Web cocitations
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accuracy algorithm 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 fe-means feature selection fetch flyfactory focused crawler fraction given Google graph HITS HITS algorithm homepage hub scores hyperlinks hypertext Internet inverted index iterations keyword large number learner linear link-based machine learning mapping matrix measure naive Bayes classifier nodes number of clusters outlinks PageRank parameters parse Pr(c precision probabilistic probability problem query terms random ranking recall relevant representation represented response root set sample search engines Section semisupervised server shown in Figure similarity socket space specific structure subgraph subtree supervised learning tagging techniques term distribution test document textual TFIDF token topic directory training data training documents URLs values variable vector vector-space Web graph Web mining words Yahoo
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