关键词: k-means算法, 最大最小距离, 改进流形距离, 粗糙集, 适应度函数

Abstract: “Absolute manifold” dataset has better performance than the “relative manifold” one, the sick clustering performance while the parameter[ρ]varies with a wide range, which are the defects exited in clustering algorithm based on the manifold distance. To resolve these problems, a rough k-means clustering algorithm based on the improved manifold distance is proposed. In this algorithm, boundary data clustering problem and the clustering performance has been resolved and improved by choosing clustering center with attribute partitioning and the max-min distance method, optimizing k-means with the improved manifold distance and rough set and combining the termination of judgement conditions. The simulation results show that this algorithm can effectively improve both on the “absolute manifold” and “relative manifold” dataset clustering, and the variation of parameters has a greater impact of the clustering performance.

Key words: k-means algorithm, max-min distance, improved manifold distance, rough set, criterion function