一种基于SimRank得分的谱聚类算法

传统的谱聚类算法在建立相似度矩阵时仅考虑数据点与点的距离,忽略了数据点之间隐含的内在联系。针对这一问题,提出了一种基于SimRank的谱聚类算法。该算法首先用无向图数据建立邻接矩阵,并计算出基于SimRank的相似度矩阵；然后根据相似度矩阵建立拉普拉斯矩阵表达式,对其进行归一化后再进行谱分解；最后对分解得到的特征向量进行k-means聚类。在Zoo等UCI标准数据集上的实验结果表明,所提算法在聚类精确度、标准互信息和纯度3个评价指标上均优于现有的LRR(Low Rank Rrepresentation)等基于距离相似度的谱聚类算法。

Spectral Clustering Algorithm Based on SimRank Score

Traditional spectral clustering algorithms only consider distance between data points,ignoring their intrinsic relation.To deal with this problem,a spectral clustering method based on SimRank score was proposed.Firstly,the method computes the adjacency matrix of the undirected graph data,and obtains the similarity matrix based on SimRank.Secondly,a Laplacian matrix expression is constructed based on similarity matrix,which is then normalized followed by spectral decomposition.Finally,a k-means clustering procedure is performed on the obtained eigenvectors to obtain the final clustering results.Experimental results on benchmark datasets from UCI data repository show that the proposed algorithm is superior to the existing spectral clustering algorithms based on distance similarity in terms of clustering accuracy,standard mutual information and purity.