Information Technology Journal1812-56381812-5646Asian Network for Scientific Information10.3923/itj.2007.255.258ZhouHongfang FengBoqin LvLintao HuiYue 2200762Subspace clustering has been studied extensively and widely since traditional algorithms are ineffective in high-dimensional data spaces. Firstly, they were sensitive to noises, which are inevitable in high-dimensional data spaces; secondly, they were too severely dependent on some distance metrics, which cannot act as virtual indicators as in high-dimensional data spaces; thirdly, they often use a global threshold, but different groups of features behave differently in various dimensional subspaces. Accordingly, traditional clustering algorithms are not suitable in high-dimensional spaces. On the analysis of the advantages and disadvantages inherent to the traditional clustering algorithm, we propose a robust algorithm JPA (Joining-Pruning Algorithm). Our algorithm is based on an efficient two-phase architecture. The experiments show that our algorithm achieves a significant gain of runtime and quality in comparison to nowadays subspace clustering algorithms.]]>Cheng, C.H., A. Waichee and F.Y. Zhang,1999Kailing, K., H.P. Kriegel, P. Kroger and S. Wanka,2003Kailing, K.,2004Procopiuc, C.M., M.J. Pankaj, K. Agarwal and T.M. Murali,2002Parsons, L., E. Haque and H. Liu,2004Yang, J., W. Wang, H. Wang and P. Yu,2002