Automated Clustering of Large Data Sets Based on a Topology Representing Graph

Abstract

A powerful method in analysis of large data sets where there are many natural clusters with varying statistics such as different sizes shapes density distribution is the use of self-organizing maps (SOMs) [1]. However further processing tools such as visualization interactive clustering are often necessary to capture the clusters from the learned SOM knowledge. A recent visualization scheme CONNvis [2] and interactive clustering from CONNvis utilizes the data topology for SOM knowledge representation by using a weighted Delaunay graph CONN. In this paper an automated clustering scheme for SOMs. SOMcluster which is a two-level clustering of CONN by the skills obtained in the interactive process is proposed. It is shown that SOMcluster which does not require the number of clusters a priori is used successfully for automated segmentation of a remote sensing spectral image which has many clusters some of which were unidentified in previous works.