计算机科学与探索Issue(11)：1365-1372,8.DOI:10.3778/j.issn.1673-9418.1405038
基于有效距离的谱聚类算法
Spectral Clustering Algorithm Based on Effective Distance
摘要
Abstract
Based on existing distance metrics and the traditional spectral clustering algorithm, this paper proposes a new spectral clustering based on effective distance (SCED). Specifically, the proposed SCED algorithm uses effective distance to replace conventional Euclidean distance, by considering global properties of data that are reflected by sparse reconstruction coefficients. In effective distance, the similarity of a sample pair is evaluated by using not only the distance between these two samples, but also distances between one specific sample and other related samples. Sparse reconstruction coefficients are employed to reflect such global relationship among samples. The experimental results on ten UCI benchmark datasets demonstrate the efficiency of the proposed SCED algorithm.关键词
谱聚类/有效距离/距离度量Key words
spectral clustering/effective distance/distance metric分类
信息技术与安全科学引用本文复制引用
光俊叶,刘明霞,张道强..基于有效距离的谱聚类算法[J].计算机科学与探索,2014,(11):1365-1372,8.基金项目
The Natural Science Foundation for Distinguished Young Scholar of Jiangsu Province under Grant No. BK2013003(江苏省杰出青年科学基金） （）
the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20123218110009(高等学校博士学科点专项科研基金) （高等学校博士学科点专项科研基金）
the Fundamental Research Funds of NUAA under Grant No. NE2013105(南京航空航天大学基本科研业务费) （南京航空航天大学基本科研业务费）
