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Affinity Propagation Clustering Using Path Based Similarity

1,†, 1,† and 1,2,*
College of Computer and Information Science, Southwest University, Chongqing 400715, China
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
Author to whom correspondence should be addressed.
Current address: College of Computer and Information Science, Southwest University, Beibei, Chongqing 400715, China
Academic Editor: Javier Del Ser Lorente
Algorithms 2016, 9(3), 46;
Received: 11 June 2016 / Revised: 12 July 2016 / Accepted: 18 July 2016 / Published: 21 July 2016
PDF [1035 KB, uploaded 21 July 2016]


Clustering is a fundamental task in data mining. Affinity propagation clustering (APC) is an effective and efficient clustering technique that has been applied in various domains. APC iteratively propagates information between affinity samples, updates the responsibility matrix and availability matrix, and employs these matrices to choose cluster centers (or exemplars) of respective clusters. However, since it mainly uses negative Euclidean distance between exemplars and samples as the similarity between them, it is difficult to identify clusters with complex structure. Therefore, the performance of APC deteriorates on samples distributed with complex structure. To mitigate this problem, we propose an improved APC based on a path-based similarity (APC-PS). APC-PS firstly utilizes negative Euclidean distance to find exemplars of clusters. Then, it employs the path-based similarity to measure the similarity between exemplars and samples, and to explore the underlying structure of clusters. Next, it assigns non-exemplar samples to their respective clusters via that similarity. Our empirical study on synthetic and UCI datasets shows that the proposed APC-PS significantly outperforms original APC and other related approaches. View Full-Text
Keywords: clustering; affinity propagation; path-based similarity; complex structure clustering; affinity propagation; path-based similarity; complex structure

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Jiang, Y.; Liao, Y.; Yu, G. Affinity Propagation Clustering Using Path Based Similarity. Algorithms 2016, 9, 46.

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