Clustering web search results using fuzzy ants

Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881, De Cock, Martine, Cornelis, Chris and Kerre, Etienne E. 2007. Clustering web search results using fuzzy ants. International Journal of Intelligent Systems 22 (5) , pp. 455-474. 10.1002/int.20209

Full text not available from this repository.

Abstract

Algorithms for clustering Web search results have to be efficient and robust. Furthermore they must be able to cluster a data set without using any kind of a priori information, such as the required number of clusters. Clustering algorithms inspired by the behavior of real ants generally meet these requirements. In this article we propose a novel approach to ant-based clustering, based on fuzzy logic.We show that it improves existing approaches and illustrates how our algorithm can be applied to the problem of Web search results clustering.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Publisher:	Wiley-Blackwell
ISSN:	0884-8173
Last Modified:	20 Oct 2022 09:24
URI:	https://orca.cardiff.ac.uk/id/eprint/31816

Citation Data

Cited 16 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item