In order to solve the social network members' distribution is no longer confined to the two-dimensional plane and social network partitioning process involves groups of content privacy problems, this paper provides an improved CURE algorithm based on principal component analysis to reduce the dimension of social network division, which named DRICURE algorithm. First, the concept of principal component analysis could be used to reduce the dimension of social network distribution and simplify the calculation method. Second, the distance between nodes is measured by the closeness among members. Finally, DRICURE algorithm is used to cluster until the number of categories meets the requirements, and uses similarity to solve the attribution of outlier to members of social networks. Experiments show that the algorithm reduces the dimension of social network distribution and improves the time and space efficiency distinctly, and also does not involve the privacy of social network members. The results show that the quality of community structure obtained in this paper is higher, and the separation of isolated members is considered effectively.
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