Automated clustering of large data sets based on a topology representing graph

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). 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, 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.