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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2015

01.01.2015 | Original Article

Local k-proximal plane clustering

verfasst von: Zhi-Min Yang, Yan-Ru Guo, Chun-Na Li, Yuan-Hai Shao

Erschienen in: Neural Computing and Applications | Ausgabe 1/2015

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Abstract

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.
Vorheriger Artikel Fault feature extraction using independent component analysis with reference and its application on fault diagnosis of rotating machinery
Nächster Artikel A similarity-based mechanism to control genetic algorithm and local search hybridization to solve traveling salesman problem

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Metadaten
Titel
Local k-proximal plane clustering
verfasst von
Zhi-Min Yang
Yan-Ru Guo
Chun-Na Li
Yuan-Hai Shao
Publikationsdatum
01.01.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1707-9

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