关键词: K-均值, 聚类分析, 人工鱼群算法, 免疫接种

Abstract: The traditional K-means algorithm is over-dependent on the choice of the initial cluster centers during the clustering process. Meanwhile, due to the lack of global search capability, it is difficult to get the accurate cluster centers. Fish-school algorithm shows good parallelism and global search feature in solving optimization problems, but may fall into local optimal solution because of the artificial parameters. In this paper, it applies fish-school algorithm to the clustering problems according to their characteristics and combines immunity-vaccination mechanism with the fish-school algorithm using adaptive step to strengthen the search performance of the algorithm for the exact solution. The experimental analysis and comparison results on UCI datasets show that the algorithm has better validity and stability.

Key words: K-means, clustering analysis, artificial fish swarm algorithm, immunity-vaccination