计算机工程与应用2016，Vol.52Issue(6)：67-73,7.DOI:10.3778/j.issn.1002-8331.1405-0255
基于流形结构的多聚类中心近邻传播聚类算法
Manifold structure based multi-exemplar affinity propagation
摘要
Abstract
When dealing with arbitrary shape data set with manifold structure, multi-exemplar affinity propagation cannot obtain good clustering results. To overcome this shortcoming, this paper designs a brand new similarity measure based on the idea of manifold learning. This similarity can amplify the similarity between data points of the same manifold and reduce the similarity between data points of different manifolds. As a result, the similarity matrix can reflect the internal manifold structure of the data set precisely. Based on this similarity matrix, this paper proposes the novel manifold structure based multi-exemplar affinity propagation, which can solve the problem mentioned above effectively and also improve the effi-ciency of this algorithm. It obtains promising results both on artificial datasets and USPS handwritten digits datasets. The simulation results show that the new method outperforms traditional MEAP algorithm.关键词
近邻传播聚类/多聚类中心近邻传播聚类/基于密度的聚类/流形结构/相似性度量Key words
affinity propagation/multi-exemplar affinity propagation/density-based clustering/manifold structure/simi-larity measure分类
信息技术与安全科学引用本文复制引用
陈雷雷,葛洪伟,杨金龙,袁运浩..基于流形结构的多聚类中心近邻传播聚类算法[J].计算机工程与应用,2016,52(6):67-73,7.基金项目
国家自然科学基金（No.61305017，No.60975027）；江苏省自然科学基金（No.BK20130154）；江苏高校优势学科建设工程资助项目。 （）
