Fast and Intuitive Clustering of Web Documents

Fast and Intuitive Clustering of Web Documents (1997)

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by Oren Zamir , Oren Etzioni , Omid Madani , Richard M. Karp

Venue:	In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining
Citations:	87 - 2 self

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Datum	Value	Source
TITLE	Fast and Intuitive Clustering of Web Documents	user correction - Legacy Corrections
AUTHOR NAME	Oren Zamir	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science & Engineering	SVM HeaderParse 0.2
AUTHOR ADDR	University of Washington Box 352350; Seattle, WA 98195-2350	SVM HeaderParse 0.1
AUTHOR NAME	Oren Etzioni	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science & Engineering	SVM HeaderParse 0.2
AUTHOR ADDR	University of Washington Box 352350; Seattle, WA 98195-2350	SVM HeaderParse 0.1
AUTHOR NAME	Omid Madani	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science & Engineering	SVM HeaderParse 0.2
AUTHOR ADDR	University of Washington Box 352350; Seattle, WA 98195-2350	SVM HeaderParse 0.1
AUTHOR NAME	Richard M. Karp	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science & Engineering	SVM HeaderParse 0.2
AUTHOR ADDR	University of Washington Box 352350; Seattle, WA 98195-2350	SVM HeaderParse 0.1
ABSTRACT	Conventional document retrieval systems (e.g., Alta Vista) return long lists of ranked documents in response to user queries. Recently, document clustering has been put forth as an alternative method of organizing retrieval results (Cutting et al. 1992). A person browsing the clusters can discover patterns that could be overlooked in the traditional presentation. This paper describes two novel clustering methods that intersect the documents in a cluster to determine the set of words (or phrases) shared by all the documents in the cluster. We report on experiments that evaluate these intersectionbased clustering methods on collections of snippets returned from Web search engines. First, we show that word-intersection clustering produces superior clusters and does so faster than standard techniques. Second, we show that our O(n log n) time phrase-intersection clustering method produces comparable clusters and does so more than two orders of magnitude faster than all methods tested. I...	user correction - Legacy Corrections
YEAR	1997	INFERENCE
VENUE	In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	287--290	INFERENCE
CITATIONS	9 found	ParsCit 1.0