Technical gazette, Vol. 23 No. 6, 2016.
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
Publication date:
29.11.2016.
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