Automatic web site classification in a large repository under information filtering and retrieval techniques

Anagnostopoulos, I; Kouzas, G; Anagnostopoulos, C; Vergados, D; Papaleonidopoulos, I; Generalis, A; Loumos, V; Kayafas, E

dc.contributor.author	Anagnostopoulos, I	en
dc.contributor.author	Kouzas, G	en
dc.contributor.author	Anagnostopoulos, C	en
dc.contributor.author	Vergados, D	en
dc.contributor.author	Papaleonidopoulos, I	en
dc.contributor.author	Generalis, A	en
dc.contributor.author	Loumos, V	en
dc.contributor.author	Kayafas, E	en
dc.date.accessioned	2014-03-01T02:42:04Z
dc.date.available	2014-03-01T02:42:04Z
dc.date.issued	2002	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30764
dc.subject	E-commerce	en
dc.subject	Information system	en
dc.subject	Knowledge segmentation	en
dc.subject	Portal	en
dc.subject	Web-page classification	en
dc.subject.other	Database systems	en
dc.subject.other	Electronic commerce	en
dc.subject.other	HTML	en
dc.subject.other	Information retrieval systems	en
dc.subject.other	Knowledge representation	en
dc.subject.other	Management information systems	en
dc.subject.other	Search engines	en
dc.subject.other	Information filtering	en
dc.subject.other	Information system	en
dc.subject.other	Knowledge segmentation	en
dc.subject.other	Web site classification	en
dc.subject.other	Websites	en
dc.title	Automatic web site classification in a large repository under information filtering and retrieval techniques	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/MELECON.2002.1014574	en
heal.identifier.secondary	http://dx.doi.org/10.1109/MELECON.2002.1014574	en
heal.publicationDate	2002	en
heal.abstract	In nowadays, there is a huge volume of information on the web, which is disseminated to the users in a chaotic way. In order to be easily accessed, the information must be clustered and classified in appropriate knowledge areas. Thus, many heavily visited sites or Portals try to unify the access to multiple information sources, providing by this way classification of information. This paper proposes a system, aiming to classify e-commerce sites according their web-content. This system can be implemented for automatic knowledge segmentation in a Portal or in a search engine repository. The system performance reached 96% in the first test sets, after the learning phase. However, the performance significantly increases (up to 98%) as the number of the test sets increases.	en
heal.journalName	Proceedings of the Mediterranean Electrotechnical Conference - MELECON	en
dc.identifier.doi	10.1109/MELECON.2002.1014574	en
dc.identifier.volume	1	en
dc.identifier.spage	279	en
dc.identifier.epage	283	en