doi:

DOI: 10.3724/SP.J.1146.2009.01066

Journal of Electronics & Information Technology (电子与信息学报) 2010/32:3 PP.509-514

Affinity Propagation Clustering Based on Variable-Similarity Measure


Abstract:
Affinity Propagation (AP) clustering is not fit to deal with multi-scale data cluster as well as the arbitrary shape cluster issue. Therefore, an improved affinity propagation clustering algorithm AP-VSM (Affinity Propagation based on Variable-Similarity Measure) is proposed embarking from the token of data distribution characters. First, a kind of variable-similarity measure method is devised according of characters of global and local data distribution, which has the ability of describing the characters of data clustering effectively. Then AP-VSM clustering algorithm is proposed base on the frame of traditional AP algorithm, and this method has extended data processing capacity compared with traditional AP. The simulation results show that the new method is outperforming traditional AP algorithm.

Key words:Data processing,Cluster analysis,Affinity Propagation (AP) clustering,Variable-similarity measure,Manifold analysis

ReleaseDate:2014-07-21 15:13:07



[1] Frey B J and Dueck D. Clustering by passing messages between data points. Science, 2007, 315(5814): 972-976.

[2] Givoni I E and Frey B J. A binary variable model for affinity propagation. Neural Computation, 2009, 21(6): 1589-1600.

[3] Jia Sen, Qian Yun-tao, and Ji Zhen. Band selection for hyperspectral imagery using affinity. Propagation. Proceedings of the 2008 Digital Image Computing: Techniques and Applications, Canberra, ACT, 1-3.12.2008: 137-141.

[4] Gang Li, Lei Guo, and Liu Tian-ming, et al. Grouping of brain MR images via affinity propagation. IEEE International Symposium on Circuits and Systems, 2009 (ISCAS 2009) Taipei, Taiwan, 5.24. 2009: 2425-2428.

[5] Dueck D, Frey B J, and Jojic N, et al. Constructing treatment portfolios using affinity propagation[C]. Proceedings of 12th Annual International Conference, RECOMB 2008. Singapore. 3.30-4.2, 2008: 360-371.

[6] Leone M, Sumedha, and Weigt M. Clustering by soft-constraint affinity propagation: applications to gene- expression data. Bioinformatics, 2007, 23(20): 2708-2715.

[7] 王开军, 张军英, 李丹等. 自适应仿射传播聚类. 自动化学报, 2007, 33(12): 1242-1246. Wang Kai-jun, Zhang Jun-ying, and Li Dan. Adaptive affinity propagation clustering. Acta Automatica Sinica, 2007, 33(12): 1242-1246.

[8] 王玲, 薄列峰, 焦李成. 密度敏感的半监督谱聚类. 软件学报, 2007, 18(10): 2412-2422. Wang L, Bo L F, and Jiao L C. Density-Sensitive semi-supervised spectral clustering. Journal of Software, 2007, 18(10): 2412-2422.

[10] Little M A, McSharry P E, Hunter E J, and Lorraine O. Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE Transactions on Biomedical Engineering, 2009, 56(4): 1015-1022.