Mining the Web: Analysis of Hypertext and Semi Structured Data

@book{citeulike:111664, abstract = {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.<br><br>* A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining.<br>* Details the special challenges associated with analyzing unstructured and semi-structured data.<br>* Looks at how classical Information Retrieval techniques have been modified for use with Web data.<br>* Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning.<br>* Analyzes current applications for resource discovery and social network analysis.<br>* An excellent way to introduce students to especially vital applications of data mining and machine learning technology.</li></ul>}, author = {Chakrabarti, Soumen }, citeulike-article-id = {111664}, comment = {Another Chakrabarti title is: Mining the Web: discovering knowledge from hypertext data, or are they the same book? author = {Soumen Chakrabarti}, title = {Mining the {Web}: Discovering Knowledge from Hypertext Data}, publisher = {Morgan-Kauffman}, year = 2002, isbn = {ISBN 1-55860-754-4}, url = {http://www.cse.iitb.ac.in/\~{}soumen/mining-the-web/}}, howpublished = {Hardcover}, isbn = {1558607544}, keywords = {citeseer, discovering\_communities, parts\_of\_speech, resource\_discovery, semiautomatic\_resource\_discovery, sense\_tagging, social\_network\_analysis}, month = {August}, posted-at = {2005-09-22 23:59:50}, priority = {0}, publisher = {Morgan Kaufmann}, series = {Data Management Systems?}, title = {Mining the Web: Analysis of Hypertext and Semi Structured Data}, url = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&amp;path=ASIN/1558607544}, year = {2002} }

TY - BOOK ID - citeulike:111664 L3 - citeulike-article-id:111664 TI - Mining the Web: Analysis of Hypertext and Semi Structured Data SE - Data Management Systems? PB - Morgan Kaufmann SN - 1558607544 N2 - 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.<br><br>* A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining.<br>* Details the special challenges associated with analyzing unstructured and semi-structured data.<br>* Looks at how classical Information Retrieval techniques have been modified for use with Web data.<br>* Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning.<br>* Analyzes current applications for resource discovery and social network analysis.<br>* An excellent way to introduce students to especially vital applications of data mining and machine learning technology.</li></ul> KW - citeseer KW - discovering_communities KW - parts_of_speech KW - resource_discovery KW - semiautomatic_resource_discovery KW - sense_tagging KW - social_network_analysis N1 - Another Chakrabarti title is: Mining the Web: discovering knowledge from hypertext data, or are they the same book? author = {Soumen Chakrabarti}, title = {Mining the {Web}: Discovering Knowledge from Hypertext Data}, publisher = {Morgan-Kauffman}, year = 2002, isbn = {ISBN 1-55860-754-4}, url = {http://www.cse.iitb.ac.in/~soumen/mining-the-web/} AU - Chakrabarti, Soumen PY - 2002/08/15/ UR - http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&amp;path=ASIN/1558607544 ER -