吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

基于变异赋权的吸引子传播算法

韩旭明1, 孙海波2, 王丽敏3   

  1. 1. 长春工业大学 软件学院, 长春 130012; 2. 吉林财经大学 经济模拟研究所, 长春 130117;3. 吉林财经大学 管理科学与信息工程学院, 长春 130117
  • 收稿日期:2014-01-25 出版日期:2014-05-26 发布日期:2014-08-27
  • 通讯作者: 王丽敏 E-mail:wlm_new@163.com

Affinity Propagation Algorithm Based onCoefficient of Variation Weighting

HAN Xuming1, SUN Haibo2, WANG Limin3   

  1. 1. School of Software, Changchun University of Technology, Changchun 130012, China;2.  Institute of Economic Simulation, Jilin University of Finance and Economics, Changchun 130117, China;3. School of Management Science and Information Engineering,Jilin University of Finance and Economics, Changchun 130117, China
  • Received:2014-01-25 Online:2014-05-26 Published:2014-08-27
  • Contact: WANG Limin E-mail:wlm_new@163.com

摘要:

基于传统吸引子传播算法, 通过样本特征赋权, 克服冗余信息的影响及给出新的相似性度量方法等策略, 提出一种基于变异系数赋权的吸引子传播算法. 实验结果表明, 该算法在处理属性较多、 信息重叠的样本时, 不仅具有吸引子传播算法的快速、 高效聚类特征, 且聚类性能明显优于传统吸引子传播算法和K-均值等经典聚类算法.

关键词: 吸引子传播算法, 变异系数, 特征赋权, 聚类

Abstract:

An improved affinity propagation algorithm based on coefficient of variation was proposed via feature weighing of samples, which has overcome the impact of redundant information, and the new similarity measure method was proposed. The experimental results show that the proposed algorithm is not only quick and efficient but also better than the traditional affinity propagation algorithm and the classical Kmeans method for clustering when it was used to process the samples with more characteristics and attributes, and information overlap.

Key words: affinity propagation algorithm, coefficient of variation, feature weighing, clustering

中图分类号: 

  • TP181