关键词: SUBCLU, 子空间聚类, 高维数据, 兴趣子空间

Abstract: SUBCLU algorithm is a subspace clustering algorithm for high dimensional data. However, it produces a lot of intermediate clusters during the iteration of finding maximum interesting subspace clusters by using bottom-up search strategy. A large amount of time is consumed in the process of generating these intermediate clusters. Focus on this problem, algorithm BDFS-SUBCLU （the deep-first search with back-trace-based SUBCLU） is proposed. To avoid producing the intermediate clusters and reduce the time complexity, this algorithm uses deep-first search with back-trace to find maximum interesting subspace clusters. To avoid that the adjacent clusters affected by those special data points merge to one, BDFS-SUBCLU constraints the key point in every subspace. The experiments conducted on synthetic datasets and real datasets show that BDFS-SUBCLU improves efficiency and accuracy compared to SUBCLU.

Key words: SUBCLU, subspace clustering, high dimensional data, interesting subspace