Combining Unsupervised and Supervised Neural Networks in Cluster Analysis of Gamma-Ray Burst

Abstract

The paper proposes the use of Kohonen's Self Organizing Map (SOM), and supervised neural networks to find clusters in samples of gamma-ray burst (GRB) using the measurements given in BATSE GRB. The extent of separation between clusters obtained by SOM was examined by cross validation procedure using supervised neural networks for classification. A method is proposed for variable selection to reduce the "curse of dimension-ality". Six variables were chosen for cluster analysis. Additionally, principal components were computed using all the original variables and 6 components which accounted for a high percentage of variance was chosen for SOM analysis. All these methods indicate 4 or 5 clusters. Further analysis based on the average profiles of the GRB indicated a possible reduction in the number of clusters.