关键词: CABOSFV算法, 高维数据, 稀疏特征, 聚类

Abstract: CABOSFV is an efficient algorithm based on sparse feature for high dimensional data clustering.However the clustering quality of the algorithm is sensitive to the order of input data.To this problem，improved CABOSFV clustering considering data sort（CABOSFV_CS） is proposed，which describes the sparse feature of data by defining a new concept sparseness index and improves the clustering quality of CABOSFV by sorting data according to the ascending sequence of sparseness index.UCI benchmark data sets are used to compare CABOSFV_CS with traditional CABOSFV algorithm.The empirical tests show that CABOSFV_CS increases the clustering accuracy effectively.

Key words: CABOSFV algorithm, high dimensional data, sparse feature, clustering