Kernel K-means clustering optimized by bare bones differential evolution algorithm
Xinping Zhang, Jie Liu, Xiaoyuan Zhang · 2014
The traditional k-mean clustering method is sensitive to the initial clustering centers and easy to fall into local optimum solution. To overcome this problem a novel kernel clustering analysis method based on an almost parameter-free evolutionary algorithm, bare bones differential evolution (BBDE), is proposed in this paper. The constituent elements of the proposed method and its general steps to solve problems are described in detail. Some UCI datasets are used to evaluate the proposed method. Experiment results show that the proposed method has a good performance in clustering problems.