Abstract
The affinity propagation clustering is a new clustering algorithm. The volatility is introduced to measure the degree of the numerical oscillations. The research focuses on two main parameters of affinity propagation: preference and damping factor, and considers their relation with the numerical oscillating and volatility, and we find that the volatility can be reduced by increasing the damping factor or preference, which provides the basis for eliminating the numerical oscillating.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972–976
Frey BJ, Dueck D (2008) Response to comment on “clustering by passing messages between data points”. Science 319(5864):726
Leone M, Sumedha S, Weigt M (2007) Clustering by soft-constraint affinity propagation: applications to gene-expression data. Bioinformatics 23(20):2708–2715
Sumedha ML, Weigt M (2008) Unsupervised and semi-supervised clustering by message passing: Soft-constrain affinity propagation. Eur Phys J B 66:125–135
Wang K, Zhang J, Li D, Zhang X, Guo T (2007) Adaptive affinity propagation clustering. J Acta Automatica Sinica, 33(12): 1242–1246, (In Chinese)
Yu X, Yu J (2008) Semi-supervised clustering based on affinity propagation algorithm. J Software, 19(11):2803–2813, (In Chinese)
Zhang X, Wang W, Nørvåg K, Sebag M (2010) K-AP: generating specified K clusters by efficient affinity propagation. ICDM 2010: 1187–1192
Acknowledgments.
This research was supported by the grants from the Natural Science Foundation of China (No. 71271209); Huaiyin Normal University Youth Talents Support Project (NO. 11HSQNZ18).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Gui, B., Yang, X. (2014). Research on Parameters of Affinity Propagation Clustering. In: Huang, YM., Chao, HC., Deng, DJ., Park, J. (eds) Advanced Technologies, Embedded and Multimedia for Human-centric Computing. Lecture Notes in Electrical Engineering, vol 260. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7262-5_72
Download citation
DOI: https://doi.org/10.1007/978-94-007-7262-5_72
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-7261-8
Online ISBN: 978-94-007-7262-5
eBook Packages: EngineeringEngineering (R0)