Original scientific paper

https://doi.org/10.17559/TV-20160531025240

Semi-supervised affinity propagation based on density peaks

Limin Wang ; School of Management Science and Information Engineering, Jilin University of Finance and Economics, 3699 Jingyue Street, Changchun 130117, China
Xing Tao ; School of Management Science and Information Engineering, Jilin University of Finance and Economics, 3699 Jingyue Street, Changchun 130117, China
Xuming Han ; School of Computer Science and Engineering, Changchun University of Technology, 307, Teaching Building, No. 7186, Weixing Road, Changchun 130012, China
Jialing Han ; School of Computer Science and Engineering, Changchun University of Technology, 307, Teaching Building, No. 7186, Weixing Road, Changchun 130012, China
Ying Liu ; School of Computer Science and Engineering, Changchun University of Technology, 307, Teaching Building, No. 7186, Weixing Road, Changchun 130012, China
Guangyu Mu ; School of Computer Science and Engineering, Changchun University of Technology, 307, Teaching Building, No. 7186, Weixing Road, 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, a semi-supervised affinity propagation clustering algorithm based on density peaks (SAP-DP) was proposed in this paper. The algorithm uses a new algorithm of density peaks (DP) which has the advantage of the manifold clustering with the idea of semi-supervised, builds pairwise constraints to adjust the similarity matrix, and then executes the AP clustering. The results of the simulation experiments validated that the proposed algorithm has better clustering performance compared with conventional AP.

Keywords

Affinity Propagation; Density Peaks; pairwise constraints; semi-supervised learning

Hrčak ID:

169536

URI

https://hrcak.srce.hr/169536

Publication date:

29.11.2016.

Article data in other languages: croatian

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