caa a22 4500 606158944 CHVBK 20210128100622.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00521-014-1707-9 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1707-9 Local k -proximal plane clustering [Elektronische Daten] [Zhi-Min Yang, Yan-Ru Guo, Chun-Na Li, Yuan-Hai Shao] k-Plane clustering (kPC) and k-proximal plane clustering (kPPC) cluster data points to the center plane, instead of clustering data points to cluster center in k-means. However, the cluster center plane constructed by kPC and kPPC is infinitely extending, which will affect the clustering performance. In this paper, we propose a local k-proximal plane clustering (LkPPC) by bringing k-means into kPPC which will force the data points to center around some prototypes and thus localize the representations of the cluster center plane. The contributions of our LkPPC are as follows: (1) LkPPC introduces localized representation of each cluster center plane to avoid the infinitely confusion. (2) Different from kPPC, our LkPPC constructs cluster center plane that makes the data points of the same cluster close to both the same center plane and the prototype, and meanwhile far away from the other clusters to some extent, which leads to solve eigenvalue problems. (3) Instead of randomly selecting the initial data points, a Laplace graph strategy is established to initialize the data points. (4) The experimental results on several artificial datasets and benchmark datasets show the effectiveness of our LkPPC. The Natural Computing Applications Forum, 2014 k -Plane clustering nationallicence k -Proximal plane clustering nationallicence k -Means nationallicence Eigenvalue problem nationallicence Laplace graph nationallicence Yang Zhi-Min Zhijiang College, Zhejiang University of Technology, 310024, Hangzhou, People's Republic of China aut Guo Yan-Ru College of Science, Zhejiang University of Technology, 310024, Hangzhou, People's Republic of China aut Li Chun-Na Zhijiang College, Zhejiang University of Technology, 310024, Hangzhou, People's Republic of China aut Shao Yuan-Hai Zhijiang College, Zhejiang University of Technology, 310024, Hangzhou, People's Republic of China aut Neural Computing and Applications Springer London 26/1(2015-01-01), 199-211 0941-0643 26:1<199 2015 26 521 https://doi.org/10.1007/s00521-014-1707-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1707-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Yang Zhi-Min Zhijiang College, Zhejiang University of Technology, 310024, Hangzhou, People's Republic of China aut NATIONALLICENCE 700 1- Guo Yan-Ru College of Science, Zhejiang University of Technology, 310024, Hangzhou, People's Republic of China aut NATIONALLICENCE 700 1- Li Chun-Na Zhijiang College, Zhejiang University of Technology, 310024, Hangzhou, People's Republic of China aut NATIONALLICENCE 700 1- Shao Yuan-Hai Zhijiang College, Zhejiang University of Technology, 310024, Hangzhou, People's Republic of China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/1(2015-01-01), 199-211 0941-0643 26:1<199 2015 26 521