Cluster Analysis Based on Bipartite Network

Abstract EN

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 c 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.