How to efficiently specify the `correct' number of clusters from a given multidimensional data set is one of the most fundamental and unsolved problems in cluster analysis. In this paper, we propose a method for automatically discovering the number of clusters and estimating the locations of the centroids of the resulting clusters. This method is base on the interpretation of a self-organizing feature map (SOFM) formed by the given data set. The other difficult problem in cluster analysis is how to choose an appropriate metric for measuring the similarity between a pattern and a cluster centroid. The performance of clustering algorithms greatly depends on the chosen measure of similarity. Clustering algorithms utilizing the Euclidean metric view patterns as a collection of hyperspherical-shaped swarms. Actually, genetic structures of real data sets often exhibit hyperellipsoidal-shaped clusters. In the second part of this paper we present a method of training a single-layer neural network composed of quadratic neurons to cluster data into hyperellipsoidal- and/or hyperspherical-shaped swarms. Two data sets are utilized to illustrate the proposed methods.
|Number of pages||6|
|Journal||Proceedings of the IEEE International Conference on Systems, Man and Cybernetics|
|State||Published - 1997|
|Event||Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 1 (of 5) - Orlando, FL, USA|
Duration: 12 Oct 1997 → 15 Oct 1997