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Clustering web search results using fuzzy ants

Schockaert, Steven, 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

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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: 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: 04 Jun 2017 04:03
URI: http://orca.cf.ac.uk/id/eprint/31816

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