Computer Science ›› 2018, Vol. 45 ›› Issue (1): 292-296.doi: 10.11896/j.issn.1002-137X.2018.01.051

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Density Clustering Algorithm Based on Laplacian Centrality

YANG Xu-hua, ZHU Qin-peng and TONG Chang-fei   

  • Online:2018-01-15 Published:2018-11-13

Abstract: As an important tool of data mining,clustering analysis can measure similarity between the different data and classify them into different categories.It is wisely applied in pattern recognition,economics,biology and so on.In this paper,a new clustering algorithm was proposed.Firstly,dataset to be classified is converted into a weighted complete graph.Data point is a node and the distance between two data points is used as weight of side between these two data points.Secondly,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 the node with higher Laplacian centrality has larger distance.Finally,the algorithm is a real parameter-free clustering method,which can classify the dataset automatically without any priori parameters.In this article,the new algorithm was compared with 9 famous clustering algorithms in 6 datasets.Experimental results show that the proposed algorithm has good clustering performance.

Key words: Weighted complete graph,Laplacian centrality,Density clustering

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