Parameter-free Laplacian centrality peaks clustering

Abstract As an important tool of data mining, clustering analysis can measure similarity between different data and classify them. It is widely applied in many fields such as pattern recognition, economics and biology. In this paper, we propose a new clustering algorithm. First, original unclassified dataset is converted into a weighted complete graph in which a node represents a data point and distance between two data points is used as weight of the edge between the corresponding two nodes. Second, local importance of each node in the network is calculated and evaluated by Laplacian centrality. The cluster center has higher Laplacian centrality than surrounding neighbor nodes and relatively large distance from nodes with higher Laplacian centralities. The new algorithm is a true parameter-free clustering method. It can automatically classify the dataset without any priori parameters. In this paper, the new algorithm was compared with 8 well-known clustering algorithms in 7 real datasets. Results show that the proposed algorithm has good clustering effect.

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