计算机工程与应用2017，Vol.53Issue(14)：130-137,8.DOI:10.3778/j.issn.1002-8331.1609-0111
一种改进的SUBCLU高维子空间聚类算法
Improved SUBCLU subspace clustering algorithm for high dimen-sional data
摘要
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 prob-lem, 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 max-imum 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 data-sets show that BDFS-SUBCLU improves efficiency and accuracy compared to SUBCLU.关键词
SUBCLU/子空间聚类/高维数据/兴趣子空间Key words
SUBCLU/subspace clustering/high dimensional data/interesting subspace分类
信息技术与安全科学引用本文复制引用
罗靖,钱雪忠,韩利钊,宋威..一种改进的SUBCLU高维子空间聚类算法[J].计算机工程与应用,2017,53(14):130-137,8.基金项目
中央高校基础研究项目资助(No.JUSRP51510,No.JUSRP51635B). （No.JUSRP51510,No.JUSRP51635B）
