A SPECTRAL CLUSTERING ALGORITHM BASED ON SELF-ADAPTION

摘要：

In traditional spectral clustering algorithms, the number of cluster is choosen in advance.A self-adaption spectral clustering algorithm is proposed to decide the cluster number automatically, which eliminates the drawbacks of two kinds of spectral clustering methods.In our algorithm, eigengap is used to discover the clustering stability and decide the cluster number automatically.We prove theoretically the rationality of cluster number using matrix perturbation theory.A kernel based fuzzy c-means is introduced to spectral clustering algorithm to separate clusters.Finally the experiments prove that our algorithm tested in the LCI data sets may get better results than c-means, Ng et.als algorithm and Francesco et.als algorithm.

关键词： Spectral clustering Eigengap Kernel

作者: KAN LI YU-SHU LIU

作者单位: School of Computer Science and Technology, Beijing Institute of Technology,Beijing, 100081, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 3965-3968

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）