[CLB09] Block clustering for web pages categorization

Conférence Internationale avec comité de lecture : Intelligent Data Engineering and Automated Learning - IDEAL 2009, September 2009, Vol. 5788, pp.260-267, Series Lecture Notes in Computer Science, Burgos, Espagne, (DOI: 10.1007/978-3-642-04394-9_32)

Auteurs: M. Charrad , Y. Lechevallier , M. Ben Ahmed , G. Saporta

Mots clés: Text Mining, Block Clustering, Categorization, Clustering, Machine Learning, Data Mining, Web Mining, Natural Language Processing

Résumé: With the growth of web-based applications and the increased popularity of the World Wide Web (WWW), the WWW became the greatest source of information available in the world leading to an increased difficulty of extracting relevant information. Moreover, the content of web sites is constantly changing leading to continual changes in Web usersâ€™ behaviours. Therefore, there is significant interest in analysing web content data to better serve users. Our proposed approach, which is grounded on automatic textual analysis of a web site independently from the usage attempts to define groups of documents dealing with the same topic. Both document clustering and word clustering are well studied problems. However, most existing algorithms cluster documents and words separately but not simultaneously. In this paper, we propose to apply a block clustering algorithm to categorize a web site pages according to their content. We report results of our recent testing of CROKI2 algorithm on a tourist web site.

Equipe: msdma

BibTeX

@inproceedings {
	CLB09,
	title	=	"{Block clustering for web pages categorization}",
	author	=	" M. Charrad and Y. Lechevallier and M. Ben Ahmed and G. Saporta ",
	booktitle	=	"{Intelligent Data Engineering and Automated Learning - IDEAL 2009}",
	year	=	2009,
	edition	=	"Springer",
	month	=	"September",
	series	=	"Lecture Notes in Computer Science",
	volume	=	5788,
	pages	=	"260-267",
	address	=	"Burgos, Espagne",
	doi	=	"10.1007/978-3-642-04394-9_32",
}