Applied Research of Weighted K-Means Algorithm in Social Networks

Abstract

Social network is a collection of heterogeneous multi-relational data represented by the graph, whose nodes represent object, whose edges represent relationships between nodes, and the weights represent the extent of the relationship between nodes. This paper gave a weighted K-means algorithm and introduced weighted K-means algorithm into social networks. Traditional k-means and most k-means variants are still computationally expensive for large datasets, however, the weighted K-means algorithm is to reduce the initial cluster centers blindness and randomness by eliminating noise point and narrowing the range of k values. Experiments datasets show that the weighted K-means algorithm significantly enhances the clustering quality. Therefore, the weighted K-means algorithm is effective and suitable for the social network. Algorithm’s error rate is smaller and accuracy is higher than that of traditional k-means algorithm.