The performance of original affinity propagation (AP) clustering algorithm is greatly influenced by an important parameter: preference (median of similarities between data points), and it may be difficult to identify complex structure data. To address the afore-mentioned issues, this paper proposes two novel methods namely the constraint rules-based affinity propagation (CRAP) and matching micro-clusters hierarchical clustering algorithm (MMHC). The CRAP algorithm can obtain better results by searching the optimal preference value by means of the constraint rules-based search algorithm (CRS). The MMHC algorithm initially takes results of AP as micro-clusters, then they are matched in order to achieve the right partitions of complex structure data. Experimental results demonstrate that the improved clustering algorithm performs better than AP.
Affinity propagation, Constraint rules, Micro-clusters hierarchical clustering.
Li-min WANG, You ZHOU, Xu-ming HAN, Yi-zhang WANG, Jing-lin YU, Shuai WANG, "Constraint Rules and Matching Micro-clusters Based Affinity Propagation Clustering Algorithm", Studies in Informatics and Control, ISSN 1220-1766, vol. 29(3), pp. 353-362, 2020. https://doi.org/10.24846/v29i3y202008
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