Affinity Propagation Clustering Based on Variable-Similarity Measure

Dong Jun; Wang Suo-ping; Xiong Fan-lun

doi:10.3724/SP.J.1146.2009.01066

Volume 32 Issue 3

Aug. 2010

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2010 > 32(3): 509-514

Dong Jun, Wang Suo-ping, Xiong Fan-lun. Affinity Propagation Clustering Based on Variable-Similarity Measure[J]. Journal of Electronics & Information Technology, 2010, 32(3): 509-514. doi: 10.3724/SP.J.1146.2009.01066

Citation:

Dong Jun, Wang Suo-ping, Xiong Fan-lun. Affinity Propagation Clustering Based on Variable-Similarity Measure[J]. Journal of Electronics & Information Technology, 2010, 32(3): 509-514. doi: 10.3724/SP.J.1146.2009.01066

Citation:

PDF( 268 KB)

Affinity Propagation Clustering Based on Variable-Similarity Measure

doi: 10.3724/SP.J.1146.2009.01066

Received Date: 2009-08-05
Rev Recd Date: 2010-01-13
Publish Date: 2010-03-19

Abstract

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.

FullText(HTML)

References(1)

References

Frey B J and Dueck D. Clustering by passing messagesbetween data points[J].Science.2007, 315(5814):972-976[2]Givoni I E and Frey B J. A binary variable model for affinitypropagation[J].Neural Computation.2009, 21(6):1589-1600[3]Jia Sen, Qian Yun-tao, and Ji Zhen. Band selection forhyperspectral 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 ofbrain MR images via affinity propagation. IEEEInternational 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.. Constructingtreatment 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 bysoft-constraint affinity propagation: applications to geneexpressiondata[J].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. Adaptiveaffinity 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-Sensitivesemi-supervised spectral clustering. Journal of Software, 2007,18(10): 2412-2422.[9]Alexander Hinneburg and Daniel A Keim. A generalapproach to clustering in large databases with noise[J].Knowledge and Information Systems.2003, 5(4):387-415[10]Little M A, McSharry P E, Hunter E J, and Lorraine O.Suitability of dysphonia measurements for telemonitoring ofParkinson's disease. IEEE Transactions on BiomedicalEngineering, 2009, 56(4): 1015-1022.

Relative Articles

Supplements(0)

Cited By

Proportional views