Adaptive web sites: Conceptual cluster mining

Adaptive web sites: Conceptual cluster mining (1999)

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by Mike Perkowitz , Oren Etzioni

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Datum	Value	Source
TITLE	Adaptive web sites: Conceptual cluster mining	INFERENCE
AUTHOR NAME	Mike Perkowitz	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science and Engineering, Box 352350; University of Washington, Seattle	SVM HeaderParse 0.2
AUTHOR ADDR	WA 98195	SVM HeaderParse 0.2
AUTHOR NAME	Oren Etzioni	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science and Engineering, Box 352350; University of Washington, Seattle	SVM HeaderParse 0.2
AUTHOR ADDR	WA 98195	SVM HeaderParse 0.2
ABSTRACT	The creation of a complex web site is a thorny problem in. user interface design. In IJCAI '97, we challenged the AI community to address this problem by creating adaptive web sites. In response, we investigate the problem of index page synthesis — the automatic creation of pages that facilitate a visitor's navigation of a Web site. Previous work has employed statistical methods to generate candidate index pages that are of limited value because they do not correspond to concepts or topics that are intuitive to people. In this paper we formalize index page synthesis as a conceptual clustering problem and introduce a novel approach which we call conceptual cluster mining: we search for a small number of cohesive clusters that correspond to concepts in a given concept description language L. Next, we present SGML, an algorithm schema that combines a statistical clustering algorithm with a concept learning algorithm. The clustering algorithm is used to generate seed clusters, and the concept learning algorithm to describe these seed clusters using expressions in L. Finally, we offer preliminary experimental evidence that instantiations of SGML outperform existing algorithms (e.g., COBWEB) in this domain. 1	SVM HeaderParse 0.2
YEAR	1999	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	264--269	INFERENCE
CITATIONS	16 found	ParsCit 1.0