Applied Research of Weighted K-Means Algorithm in Social Networks

Jin Gang Liu; Shu Liang Xu

doi:10.4028/www.scientific.net/AMM.667.286

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 667Applied Research of Weighted K-Means Algorithm in...

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.

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Periodical:

Applied Mechanics and Materials (Volume 667)

Pages:

286-290

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.667.286

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Online since:

October 2014

Authors:

Jin Gang Liu, Shu Liang Xu

Keywords:

Clustering, K-Means, Social Network, Weighted K-Means

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