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.
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Wang, F., Zhang, D., Bao, N. (2009). Fuzzy Document Clustering Based on Ant Colony Algorithm. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_80
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DOI: https://doi.org/10.1007/978-3-642-01510-6_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
Online ISBN: 978-3-642-01510-6
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