Comparison of dimensional reduction using the Singular Value Decomposition Algorithm and the Self Organizing Map Algorithm in clustering result of text documents

Abstract

Dimension reduction has two methods namely feature selection and feature extraction. Dimension reduction using the feature selection method has a better influence than the feature extraction method on the cluster results. However, there is still a need for feature extraction methods to reduce dimensions. For this reason, an alternative algorithm is needed from the feature extraction method. Self Organizing Map (SOM) is one of the artificial neural network models that has a special nature that is effectively able to create spatial internal representations of input data, or in general to create smaller data dimensions. This research was examining the capability of SOM compared to Singular Value Decomposition (SVD) in reducing data dimension of text documents before there were clustered by k-Means. Results show that SVD still better than SOM in cluster quality index but SOM faster than SVD in computation times.