Clustering web search results using fuzzy ants

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

• Type: Journal
• Year: 2007
• Where: IJIS
• Authors: Steven Schockaert, Martine De Cock, Chris Cornelis, Etienne E. Kerre
• Keywords: Algorithm, IJIS 2007, Priori Information, Web Search