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Revisiting K-Means and Topic Modeling, a Comparison Study to Cluster Arabic Documents

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Clustering Arabic text documents is of high importance for many Natural Language Technologies. This study uses a combined method to cluster Arabic text documents. Mainly, we use generative models and clustering techniques. The study uses Latent Dirichlet Allocation (LDA) and kmeans clustering algorithm and applies them to a news dataset used in previous similar studies. The aim of this research is twofold: it first shows that normalizing the weights in the vector space, for the documentterm matrix of the text documents, dramatically improves the quality of clusters and hence the accuracy of clustering when using k-means algorithm. The results are compared to a recent study on clustering Arabic text documents. Second, it shows that the combined method is superior...

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