Automatic Clustering with Differential Evolution Using Cluster Number Oscillation Method
Wei‐Ping Lee, Shen-Wei Chen · 2010
In this paper, an improved Differential Evolution algorithm (ACDE-O) with cluster number oscillation for automatic crisp clustering has been presented. The proposed algorithm needs no prior knowledge of the number of clusters of the data. Rather, it finds the optimal number of clusters on the processing with stable and fast convergence, cluster number oscillation mechanism will search more possible cluster number in case of bad initial cluster number caused bad clusters. Superiority of the proposed algorithm is demonstrated by comparing it with one recently developed partitional clustering algorithm. Experimental results over three real life datasets and the performance of proposed algorithm is mostly better than the other one.