SPECTRAL CLUSTERING WITH A NEW SIMILARITY MEASURE

摘要：

Spectral clustering has been receiving more and more concerns in recent years.The performance of spectral clustering algorithm depends heavily on similarity measure.By analyzing the characteristics of spectral clustering and global features of clustering structure, we propose a new similarity measure method based on Gaussian kernel function.It is relatively insensitive to the nuclear parameter and can handle multi-scale clustering issues.Experiment in the synthetic data sets and USPS handwritten datasets demonstrates the proposed algorithm is superior to the traditional one.

关键词： Data mining Spectral clustering Global consistency Similarity measure

作者: DONGHUA PAN JUAN LI

作者单位: Institute of Systems Engineering,Dalian University of Technology,Dalian,116024,China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer Technology and Development(2011第三届计算机技术与发展国际会议 ICCTD2011)

会议地点: 成都

会议语种:英文

页码: 2055-2059

在线出版日期: 2011-11-25（万方平台首次上网日期，不代表论文的发表时间）