Mining the Web: Discovering Knowledge from Hypertext Data / Edition 1

Overview

Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for extracting and 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 machine learning techniques as they relate specifically to the challenges of Web mining and provides applications of machine learning to sytematically acquire, store, and analyze data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress toward a Web that is more aware of content semantics. This thorough and forward-looking book gives the theoretical and practical foundations you need to build innovative applications for mining the Web.

Features

• A comprehensive, critical exploration of statistics-based attempts to make sense of Web data.
• 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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