Weighted Linear Loss Twin Support Vector Clustering

Abstract

Traditional point based clustering methods such as k-means [1], k-median [2], etc. work by partitioning the data into clusters based on the cluster prototype points. These methods perform poorly in case when data is not distributed around several cluster points. In contrast to these, plane based clustering methods such as k-plane clustering [3], local k-proximal plane clustering [4], etc. have been proposed in literature. These methods calculate k cluster center planes and partition the data into k clusters according to the proximity of the datapoints with these k planes.