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
Piecewise Linear Regression Based on Plane Clustering
TL;DR: The proposed method first partitions the data into multiple plane-centered clusters and then analytically compute corresponding piecewise linear functions, which are generated from plane clustering, which is truly coincident with geometrical intuition.
Journal ArticleDOI
Fuzzy semi-supervised weighted linear loss twin support vector clustering
Reshma Rastogi,Aman Pal +1 more
TL;DR: To build a robust clustering algorithm which is not sensitive to noise and outliers, the proposed formulations achieve better clustering accuracy over other state-of-the-art plane-based clustering algorithms with comparatively lesser computational time.
Journal ArticleDOI
General Plane-Based Clustering With Distribution Loss
TL;DR: This paper proposes a plane-based clustering method by introducing a new loss function to capture the data distribution precisely and proves that the general model terminates in a finite number of steps at a local or weak local optimal point.
Journal ArticleDOI
Robust k-subspace discriminant clustering
TL;DR: By introducing the L1-norm discriminant and local information to each subspace, k SDC realizes robust dimensionality reduction and clustering, and its optimization problems can be solved through an effective alternating direction method of multipliers.
Journal ArticleDOI
Multiple Flat Projections for Cross-Manifold Clustering.
TL;DR: This article proposes multiple flat projections clustering (MFPC) for cross-manifold clustering, a series of nonconvex matrix optimization problems is solved by a proposed recursive algorithm and a nonlinear version of MFPC is extended via kernel tricks to deal with a more complex cross- manifold learning situation.
References
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