The exponential growth of information available on the World Wide Web, and retrievable by search engines, has implied the necessity to develop efficient and effective methods for organizing relevant contents. In this field document clustering plays an important role and remains an interesting and challenging problem in the field of web computing. In this paper we present a document clustering method, which takes into account both contents information and hyperlink structure of web page collection, where a document is viewed as a set of semantic units. We exploit this representation to determine the strength of a relation between two linked pages and to define a relational clustering algorithm based on a probabilistic graph representation. The experimental results show that the proposed approach, called RED-clustering, outperforms two of the most well known clustering algorithm as k-Means and Expectation Maximization

Fersini, E., Messina, V., & Archetti, F. (2010). A probabilistic relational approach for web document clustering. INFORMATION PROCESSING & MANAGEMENT, 46(2), 117-130 [10.1016/j.ipm.2009.08.003].

A probabilistic relational approach for web document clustering

FERSINI, ELISABETTA;MESSINA, VINCENZINA;ARCHETTI, FRANCESCO ANTONIO
2010

Abstract

The exponential growth of information available on the World Wide Web, and retrievable by search engines, has implied the necessity to develop efficient and effective methods for organizing relevant contents. In this field document clustering plays an important role and remains an interesting and challenging problem in the field of web computing. In this paper we present a document clustering method, which takes into account both contents information and hyperlink structure of web page collection, where a document is viewed as a set of semantic units. We exploit this representation to determine the strength of a relation between two linked pages and to define a relational clustering algorithm based on a probabilistic graph representation. The experimental results show that the proposed approach, called RED-clustering, outperforms two of the most well known clustering algorithm as k-Means and Expectation Maximization
Articolo in rivista - Articolo scientifico
Relational document clustering, Relational web structure estimation
English
Fersini, E., Messina, V., & Archetti, F. (2010). A probabilistic relational approach for web document clustering. INFORMATION PROCESSING & MANAGEMENT, 46(2), 117-130 [10.1016/j.ipm.2009.08.003].
Fersini, E; Messina, V; Archetti, F
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/9497
Citazioni
  • Scopus 15
  • ???jsp.display-item.citation.isi??? 10
Social impact