基于Laplacian中心性的密度聚类算法

聚类分析是一种重要的数据挖掘工具,可以衡量不同数据之间的相似性,并把它们分到不同的类别中,在模式识别、经济学和生物学等领域有着广泛的应用。 文中提出了一种新的聚类算法。首先,把待分类的数据集转换成一个加权的完全图,每个数据点为一个节点,两个数据点之间的距离为相应两个节点之间边的权值。然后,用Laplacian中心性来计算和评价该网络每个节点的局部重要性,聚类中心为局部的密度中心,它具有比周围的邻居节点更高的Laplacian中心性,并且与具有更高Laplacian中心性的节点之间的距离也较大。新算法是一种真正的无参数聚类方法,不需要任何先验参数便可以自动地对数据集进行分类。在6种数据集中将其与9种知名聚类算法做了对比,结果显示该算法具有良好的聚类效果。

Density Clustering Algorithm Based on Laplacian Centrality

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.