Research in Social Network Based on Rough Set Clustering Algorithm

Abstract

This paper uses an improved algorithm based on rough set and K-means clustering to do research in social network, and it solves the problem of vague border. Social networks’ community is similar to the cluster analysis in data mining. However ,when the K-means clustering algorithm is used in social network,there are several disadvantages as following: how to determine the value of K,and the relations among community node and the community. This paper solves the disadvantages using rough set clustering ideas. This method is mainly used to find overlapping communities, and it can multi-anglely reflect the social network information better. The algorithm is applied to experiment data, and results show that this method improves the accuracy of community division obviously compared with other algorithms.