Fuzzy Document Clustering Based on Ant Colony Algorithm

Authors:
Fei Wang;Dexian Zhang;Na Bao
Affiliations:
School of Information, Henan University of technology, China 450001;School of Information, Henan University of technology, China 450001;School of Information, Henan University of technology, China 450001
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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Abstract

This paper proposes a method of document clustering algorithm based on Ant Colony Algorithm (ACO) and Fuzzy C-means Clustering (FCM). First, the algorithm makes use of the great ability of Ant Colony Algorithm for finding local extremum. It's derived from a basic model interpreting ant colony organization of cemeteries. The ACO Algorithm for flexibility, self-organization and robustness has been applied in a variety of areas. Taking advantage of these traits, good initial clusters are obtained at first step in our algorithm. Then, we combine these with Fuzzy C-means clustering organically. We also find out the whole distributing optimization clustering process, and achieve clustering analysis based on improved function. Experimental results show the good performance of the hybrid document clustering algorithm.