Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.813
Ant clustering is one of effective clustering methods. Compares to other clustering methods, ant clustering algorithm has one outstanding advantage and one disadvantage. The advantage is that the total numbers of cluster is generated automatically ,and the disadvantage is that its cluster result is random and its result is influenced by the input data and the parameters, which leads low quality of its cluster result. In this paper, we propose an improved ant clustering algorithm based on K-Means, which optimizes the rules of ant clustering algorithm．In our system, we also decide the proper values of parameters Pdel and Iter by training the training datasets before we cluster. Experimental results demonstrate that the proposed method has a good performance.
K-Means, Rules, Ant Clustering Algorithm, Parameters
J. Mo and Q. Chen, "Optimizing the Ant Clustering Model Based on K-Means Algorithm," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 699-702.