Silhouette Index Supervised Affinity Propagation Clustering
The "preferences" P acts as an important role in Affinity Propagation (AP) clustering algorithm and has great influence on the clustering performance. In the original AP method, the P is set at the beginning and kept fixed during the whole process. However, it is difficult to select a suitable P to obtain laudable clustering performance in practical applications. In this paper, we proposed a novel AP algorithm supervised by Silhouette Index where P is adjusted in the iterative process. After applied to 11 artificial and real datasets, the experiment results show that our method can get higher In-group Proportion values and Fowlkes-Mallows Index values, which is more superior and steady than the original AP method.
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Document Type: Research Article
Publication date: August 1, 2013
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