关键词: 拓展差异度, CABOSFV, 高维聚类, CABOSFV_D, 位集

Abstract:

CABOSFV is an effective high dimensional data clustering algorithm, while it tends to allocate data objects to larger clusters. To solve this problem, CABOSFV_D, a high dimensional data clustering algorithm based on extended dissimilarity, is proposed. An adjustment index [p] is introduced to expand the original sparse feature dissimilarity and reduce the impact of cluster size on object allocation. At the same time, the method of bit set is used to realize the CABOSFV_D, which significantly improves the efficiency of the algorithm. Finally, experiments are performed based on multiple UCI standard datasets. The results show that CABOSFV_D is superior to traditional algorithm in clustering effect and time efficiency.

Key words: extended dissimilarity, CABOSFV, high-dimensional clustering, CABOSFV_D, bit set