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Cluster Analysis Based on Bipartite Network

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  • Dawei Zhang
  • Fuding Xie
  • Dapeng Wang
  • Yong Zhang
  • Yan Sun

Abstract

Clustering data has a wide range of applications and has attracted considerable attention in data mining and artificial intelligence. However it is difficult to find a set of clusters that best fits natural partitions without any class information. In this paper, a method for detecting the optimal cluster number is proposed. The optimal cluster number can be obtained by the proposal, while partitioning the data into clusters by FCM (Fuzzy c -means) algorithm. It overcomes the drawback of FCM algorithm which needs to define the cluster number in advance. The method works by converting the fuzzy cluster result into a weighted bipartite network and then the optimal cluster number can be detected by the improved bipartite modularity. The experimental results on artificial and real data sets show the validity of the proposed method.

Suggested Citation

  • Dawei Zhang & Fuding Xie & Dapeng Wang & Yong Zhang & Yan Sun, 2014. "Cluster Analysis Based on Bipartite Network," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-9, February.
    • Handle: RePEc:hin:jnlmpe:676427
    DOI: 10.1155/2014/676427
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