Clustering by centroid drift and boundary shrinkage

Abstract

Locating the centers before assigning clustering labels is a traditional routine of clustering methods, which also limits the development of new clustering ideas. In this paper, we achieve the clustering task by firstly identifying the boundary points in the feature space, and then we shrink the boundary points to allocate the un-clustered points. Concretely, we propose a Centroid Drift (CD) metric and a Boundary Shrinkage (BS) strategy to detect boundary points in the feature space and allocate labels for un-clustered points, respectively. Both the CD and BS are closely related to the pre-computed k-nearest neighbor matrix, contributing to the decrease of algorithm parameters. Moreover, the common problems of noise points and non-uniform density distribution of data points in clustering task can also be alleviated with our proposed large value suppression and normalization of k-nearest neighbor distance techniques. The experiments on synthetic datasets, real-world face image datasets and hyperspectral images demonstrate the superiorities of our proposed clustering framework.