An Improved Spectral Clustering Algorithm Based on Neighbour Adaptive Scale
Spectral clustering algorithms have seen an explosive development over the past years and been successfully used in data mining and image segmentation. They can deal with arbitrary distribution dataset and easy to implement. But they are sensitive to the datasets which include clusters with distinctly different densities and the parameters must be selected cautiously. This paper proposes an improved spectral clustering algorithm based on neighbour adaptive scale, who fully considers the local structure of dataset using neighbour adaptive scale, which simplifies the selection of parameters and makes the improved algorithm insensitive to both density and outliers. Experimental results show that, compared with Ac-means and standard spectral clustering, our algorithm can achieve better clustering effect on artificial datasets and UCI public databases.
spectral graph theory spectral clustering neighbour adaptive scale
Ruijun Gu Jiacai Wang
School of Information Science, Nanjing Audit University, Nanjing, 211815, China
国际会议
北京
英文
233-236
2009-07-24（万方平台首次上网日期，不代表论文的发表时间）