Combining unsupervised and supervised neural networks in cluster analysis of gamma-ray burst

Pereira, Basilio de B. ; Rao, Calyampudi R. ; Oliveira, Rubens L. ; do Nascimento, Emília M. (2010) Combining unsupervised and supervised neural networks in cluster analysis of gamma-ray burst Journal of Data Science, 8 . pp. 327-338. ISSN 1680-743X

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Official URL: http://www.jdsruc.org/upload/JDS-394(2010-4-114252...

Abstract

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.

Item Type:Article
Source:Copyright of this article belongs to Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan.
Keywords:Bayesian Regularization; Clustering and Classification; Cross Validation; Multilayer Perceptron; Self Organizing Map; Supervised and Unsupervised Networks
ID Code:71918
Deposited On:28 Nov 2011 04:23
Last Modified:18 May 2016 17:24

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