Journal ArticleDOI
Local k-proximal plane clustering
TLDR
A local k-proximal plane clustering (LkPPC) is proposed 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.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.read more
Citations
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Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems
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Fuzzy least squares twin support vector clustering
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Tree-based localized fuzzy twin support vector clustering with square loss function
Reshma Rastogi,Pooja Saigal +1 more
TL;DR: The proposed clustering algorithm Tree-TWSVC has efficient learning time, achieved due to the tree structure and the formulation that leads to solving a series of systems of linear equations, and can efficiently handle large datasets and outperforms other TWSVM-based clustering methods.
Journal ArticleDOI
k-Proximal plane clustering
TL;DR: A novel plane-based clustering, called k-proximal plane clustering (kPPC), where each center plane is not only close to the objective data points but also far away from the others by solving several eigenvalue problems.
References
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Zhiquan Qi,Yingjie Tian,Yong Shi +2 more
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