ParKerC: Toolbox for Parallel Kernel Clustering Methods

Abstract

A large variety of fields such as biology, information retrieval, image segmentation needs unsupervised methods able to gather data without a priori information on shapes or locality. By investigating a parallel strategy based on overlapping domain decomposition, we present a toolbox which is a parallel implementation of two fully unsupervised kernel methods respectively based on density-based properties and spectral properties in order to treat large data sets in fields of pattern recognition.