Parameter-free Laplacian centrality peaks clustering
Parameter-free Laplacian centrality peaks clustering
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DOI：
10.1016/j.patrec.2017.10.025
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Xuhua Yang;Qin-Peng Zhu;Yujiao Huang;Jie Xiao;Lei Wang;Fei Tong
中科院分区：
文献类型：
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作者：
Xuhua Yang;Qin-Peng Zhu;Yujiao Huang;Jie Xiao;Lei Wang;Fei Tong
聚类分析作为数据挖掘的一种重要工具，可以度量不同数据之间的相似性，并对数据进行分类。它在模式识别、经济学、生物学等领域有着广泛的应用。在本文中，我们提出了一个新的聚类算法。首先，原始未分类的数据集被转换成一个加权的完全图，其中一个节点代表一个数据点，两个数据点之间的距离被用作相应的两个节点之间的边的权重。其次，计算网络中每个节点的局部重要性，并通过拉普拉斯中心度进行评估。聚类中心具有比周围邻居节点更高的拉普拉斯中心度，并且与具有更高拉普拉斯中心度的节点的距离相对较大。新算法是一种真正的无参数聚类方法。它可以自动分类数据集没有任何先验参数。在7个真实的数据集上，将该算法与8种常用的聚类算法进行了比较。实验结果表明，该算法具有较好的聚类效果。
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.