Adaptive Affinity Propagation Clustering

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dc.creatorWang, Kaijun
dc.creatorZhang, Junying
dc.creatorLi, Dan
dc.creatorZhang, Xinna
dc.creatorGuo, Tao
dc.date2008-05-08
dc.date.accessioned2026-07-07T09:37:43Z
dc.date.available2026-07-07T09:37:43Z
dc.descriptionAffinity propagation clustering (AP) has two limitations: it is hard to know what value of parameter 'preference' can yield an optimal clustering solution, and oscillations cannot be eliminated automatically if occur. The adaptive AP method is proposed to overcome these limitations, including adaptive scanning of preferences to search space of the number of clusters for finding the optimal clustering solution, adaptive adjustment of damping factors to eliminate oscillations, and adaptive escaping from oscillations when the damping adjustment technique fails. Experimental results on simulated and real data sets show that the adaptive AP is effective and can outperform AP in quality of clustering results.
dc.descriptionan English version of original paper
dc.identifierhttps://arxiv.org/abs/0805.1096
dc.identifierhttp://arxiv.org/abs/0805.1096
dc.identifierK. Wang, J. Zhang, D. Li, X. Zhang and T. Guo. Adaptive Affinity Propagation Clustering. Acta Automatica Sinica, 33(12):1242-1246, 2007
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/160558
dc.subjectArtificial Intelligence
dc.titleAdaptive Affinity Propagation Clustering
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