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
More filters
Journal ArticleDOI
Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems
TL;DR: The experimental results show that the proposed HAM algorithm is clearly superior to the standard ABC and MBO algorithms, as well as to other well-known algorithms, in terms of achieving the best optimal value and convergence speed.
Journal ArticleDOI
Fuzzy least squares twin support vector clustering
TL;DR: The experimental results show that the proposed fuzzy least squares twin support vector clustering (F-LS-TWSVC) achieves comparable clustering accuracy to that of TWSVC with comparatively lesser computational time.
Journal Article
A Survey on Text Mining in Clustering
TL;DR: This paper emphasis on the various techniques that are used to cluster the text documents based on keywords, phrases and concepts, and includes the different performance measures that were used to evaluate the quality of clusters.
Journal ArticleDOI
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
More filters
Journal ArticleDOI
Graph-based consensus clustering for class discovery from gene expression data
TL;DR: Experiments on gene expression data indicate that the first time in which GCC is applied to class discovery for microarray data can outperform most of the existing algorithms, identify the number of classes correctly in real cancer datasets, and discover the classes of samples with biological meaning.
Laplacian Twin Support Vector Machine for Semi-supervised Classication
Zhiquan Qi,Yingjie Tian,Yong Shi +2 more
TL;DR: In this article, a Laplacian twin support vector machine (Lap-TSVM) was proposed for semi-supervised classification problem, which can exploit the geometry information of the marginal distribution embedded in unlabeled data to construct a more reasonable classifier and be a useful extension of TSVM.
Journal ArticleDOI
Recent advances on support vector machines research
TL;DR: The purpose of this paper is to understand SVM from the optimization point of view, review several representative optimization models in SVMs, their applications in economics, in order to promote the research interests in both optimization-based SVMs theory and economics applications.
Journal ArticleDOI
Structural twin support vector machine for classification
Zhiquan Qi,Yingjie Tian,Yong Shi +2 more
TL;DR: This paper designs a new Structural Twin Support Vector Machine (called S-TWSVM), which unlike existing methods based on structural information uses two hyperplanes to decide the category of new data, of which each model only considers one class's structural information and closer to the class at the same time far away from the other class.
Journal ArticleDOI
Least squares recursive projection twin support vector machine for classification
TL;DR: This formulation leads to extremely simple and fast algorithm, called least squares projection twin support vector machine (LSPTSVM) for generating binary classifiers, which has comparable classification accuracy to that of PTSVM but with remarkably less computational time.