An improved method for multi-objective clustering ensemble algorithm
Ruochen Liu, Yongxin Liu, Yangyang Li · 2012
In this paper, we present a cluster algorithm which is an improvement of the multi-objective clustering ensemble algorithm (MOCLE), which is denoted as IMOCLE for short. First, we introduce a new clustering objective function to measure the individual difference in the optimization process so as to remain the diversity of the population. Then, a clustering ensemble technique is applied to MOCLE to obtain more competitive individual. The proposed algorithm can also ensure good partitions not be eliminated. The performance of the proposed algorithm has been compared with MOCLE over a suit of gene datasets. The experimental results show that, the superiority of the proposed method in terms of capability found the optimum number of clusters, and accuracy.