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

Journal of Data Science

article

Basilio de B. Pereira¹  Calyampudi R. Rao²  Rubens L. Oliveira¹  Em´ılia M. do Nascimento¹ 

[1] Federal University of Rio de Janeiro;Penn State University and AIMSCS;Brazilian Naval Research Institute

关键词: Bayesian regularization;    clustering and classification;    multilayer perceptron;   

DOI  :  10.6339/JDS.2010.08(2).394

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

The paper proposes the use of Kohonen’s Self Organizing Map (SOM), and supervised neural networks to find clusters in samples of gammaray 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 dimensionality”. 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.

【 授权许可】

CC BY