Stability-based preference selection in affinity propagation

被引:2
|
作者
Dong-Wei Chen
Jian-Qiang Sheng
Jun-Jie Chen
Chang-Dong Wang
机构
[1] Taiyuan University of Technology,College of Computer Science and Technology
[2] Beijing Institute of Technology,College of Computer
[3] Shenzhen Institute of Information Technology,Department of Software Engineering
[4] Guangzhou University,School of Computer Science and Educational Software
[5] Sun Yet-sen University,School of Mobile Information Engineering
[6] SYSU-CMU Shunde International Joint Research Institute,undefined
来源
关键词
Data clustering; Affinity propagation; Preference selection; Clustering stability; Model selection;
D O I
暂无
中图分类号
学科分类号
摘要
Recently, as one of the most popular exemplar-based clustering algorithms, affinity propagation has attracted a great amount of attention in various fields. The advantages of affinity propagation include the efficiency, insensitivity to cluster initialization and capability of finding clusters with less error. However, one shortcoming of the affinity propagation algorithm is that, the clustering results generated by affinity propagation strongly depend on the selection of exemplar preferences, which is a challenging model selection task. To tackle this problem, this paper investigates the clustering stability of affinity propagation for automatically selecting appropriate exemplar preferences. The basic idea is to define a novel stability measure for affinity propagation, based on which we can select exemplar preferences that generate the most stable clustering results. Consequently, the proposed approach is termed stability-based affinity propagation (SAP). Experimental results conducted on extensive real-world datasets have validated the effectiveness of the proposed SAP algorithm.
引用
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页码:1809 / 1822
页数:13
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