Finding User Clusters in Sina Microblog

Sina microblog has been a very popular social microblog service in recent years. However it\´s difficult to analyze the network structure of Sina microblog because of the huge amount of users. The emergence of cloud computing gives us a new approach to analyze large-scale social networks. Hadoop is a widely used cloud computing platform, several clustering algorithms such as K-means and Canopy have already been implemented on it. However, the initial cluster centers of K-means are hard to select. Canopy provides a way to choose initial centers, but it is not suitable for very large data sets, and both traditional K-means and Canopy K-means converge very slowly. This paper proposes an improved method to cluster microblog users based on their relationship. We name our method "Weight Partitioned Canopy K-means" (WPCK), implement it on Hadoop cluster, and test it along with existing methods. Experimental results show that WPCK can reduce the number of iterations by about 1/3 of traditional K-means and Canopy K-means, while their performance are almost the same.