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

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 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.

並列關鍵字

Bayesian regularization ； clustering and classification ； cross validation ； multilayer perceptron ； self organizing map ； supervised and unsupervised networks

被引用紀錄

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延伸閱讀

Abdulameer, M. H., Abdullah, S. N. H. S., & Othman, Z. A. (2014). Neural Gen Feature Selection for Supervised Learning Classifier. Research Journal of Applied Sciences, Engineering and Technology, 7(15), 3181-3187. https://www.airitilibrary.com/Article/Detail?DocID=20407467-201404-201507060017-201507060017-3181-3187
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國際替代計量

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

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