Chameleon algorithm based on dynamic nearest neighbors selection model
Wang Xiu-hua · Journal of Northwest Normal University · 2010
After the analysis for the performance of Chameleon algorithms,a new algorithm named DNMC is presented,which considers the backtracking mechanism making CNMC benefit to the decomposition after the merger.Experimental results on databases Wine and Iris demonstrate that DNNC outperforms M-Chameleon based on the evaluation metrics.Following the calculation of disparity of each attribute,it is found that some attributes have little effect on the results of clustering.Therefore,the complexity of the time can be improved if those attributes are neglected.