吉林大学学报（理学版）Issue(4)：753-757,5.DOI:10.13413/j.cnki.jdxblxb.2014.04.23
基于奇异值分解的自适应近邻传播聚类算法
Self-adaptive Affinity Propagation Clustering Algorithm Based on Singular Value Decomposition
摘要
Abstract
Aiming at the problem that affinity propagation algorithm has a difficult to deal with high-dimensional data,the authors put forward an self-adaptive affinity propagation algorithm based on singular value decomposition.With the aid of singular value decomposition introdued,the high-dimensional data were reconstructed and dimensions were reduced to eliminate the redundant information,based on which,a nonlinear function strategy was adopted to adjust the damping factor adaptively and improve the clustering performance of the algorithm.Experimental results show that the proposed algorithm has obviously better clustering performance than the traditional algorithm on clustering accuracy and the number of iterations.关键词
近邻传播聚类算法/奇异值分解/非线性函数策略/阻尼系数Key words
affinity propagation clustering algorithm/singular value decomposition/nonlinear function strategy/damping factor分类
信息技术与安全科学引用本文复制引用
王丽敏,姬强,韩旭明,黄娜..基于奇异值分解的自适应近邻传播聚类算法[J].吉林大学学报（理学版）,2014,(4):753-757,5.基金项目
国家自然科学基金(批准号:61202306)、吉林省科技厅项目(批准号:20100507 （批准号:61202306）
201215119 （）
20130522177JH)、吉林省教育厅重点规划项目(批准号:2012185)、吉林省高校新世纪优秀人才支持计划项目(批准号:2014159)和吉林财经大学青年学俊支持计划项目 （批准号:2012185）
