关键词: 相异性度量, K-中心点聚类, 有序属性, 无序属性, 混合属性

Abstract: In order to search a better dissimilarity measure of one hybrid attributes space and find more meaningful clustering distributions of some data sets，this paper presents a boosting K-medoids clustering algorithm based on feature weight optimization.After some necessary discuss，it lists the time complexity and astringency analysis of the boosting K-medoids clustering algorithm.In order to implement the feature weighting part of this clustering algorithm，it gives two different methods of optimizing feature weight and several adaptive methods of optimizing distance weighting coefficient and objective function coefficient.These optimization methods have solved the adaptive optimization problem of dissimilarity measure in some theoretical level.The new clustering algorithm can often get a better clustering quality which is validated by experiments of several UCI standard data sets.It indicates several valuable research expectations.

Key words: dissimilarity measure, K-medoids clustering, order attributes, sorted attributes, hybrid attributes