Original scientific paper

https://doi.org/10.17559/TV-20150314115623

Adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity

Limin Wang ; School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China
Qiang Ji ; School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China
Xuming Han ; School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China

Abstract

In view of the unsatisfying clustering effect of affinity propagation (AP) clustering algorithm when dealing with data sets of complex structures, an adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity (SAAP-SS) is proposed in this paper. First, a novel structural similarity is proposed by solving a non-linear, low-rank representation problem. Then we perform affinity propagation on the basis of adjusting the similarity matrix by utilizing the known pairwise constraints. Finally, the idea of fireworks explosion is introduced into the process of the algorithm. By adaptively searching the preference space bi-directionally, the algorithm’s global and local searching abilities are balanced in order to find the optimal clustering structure. The results of the experiments with both synthetic and real data sets show performance improvements of the proposed algorithm compared with AP, FEO-SAP and K-means methods.

Keywords

affinity propagation; fireworks explosion optimization; low rank representation; semi-supervised clustering; structural similarity

Hrčak ID:

156831

URI

https://hrcak.srce.hr/156831

Publication date:

27.4.2016.

Article data in other languages: croatian

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