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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Journal ArticleDOI
Fast and robust spatial fuzzy bounded k-plane clustering method for human brain MRI image segmentation
TL;DR: Zhang et al. as mentioned in this paper proposed two modifications in the conventional FkPC method, referred as fuzzy bounded k-plane clustering method with local spatial information (FBkPC_S1).
Dissertation
Time-efficient variants of twin support vector machine with applications in image processing
Book ChapterDOI
TWSVM for Unsupervised and Semi-supervised Learning
TL;DR: This chapter discusses twin support vector machine based algorithms for unsupervised and semi-supervised framework that can produce considerable improvement in learning accuracy.
Journal ArticleDOI
Fuzzy k-plane clustering method with local spatial information for segmentation of human brain MRI image
TL;DR: In this article, the authors incorporated local spatial information in the Fuzzy k-plane clustering method to handle the noise present in the image and showed that the proposed FkPC_S method is superior in comparison with 10 related methods in the presence of noise.
Journal ArticleDOI
MPMSVC: Multiple Parametric-Margin Support Vector Clustering
TL;DR: Wang et al. as discussed by the authors proposed an unsupervised multiple parametric-margin support vector clustering (MPMSVC) for noisy clustering tasks, where the primal of MPMSVC is enhanced in the least square sense, which enjoys an effective learning procedure.
References
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Journal ArticleDOI
A tutorial on spectral clustering
TL;DR: In this article, the authors present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches, and discuss the advantages and disadvantages of these algorithms.