Stemming and similarity measures for Arabic Documents Clustering

Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (TR) systems especially with the rapid growth of the number of online documents present in Arabic language. Document clustering aims to automatically group similar documents in one cluster using different similarity/distance measures. In this paper, we evaluate the impact of the stemming on the Arabic Text Document Clustering with five similarity/distance measures: Euclidean Distance, Cosine Similarity, Jaccard Coefficient, Pearson Correlation Coefficient and Averaged Kullback-Leibler Divergence, for the testing dataset. Our experiments on this latter show that the use of the stemming will not yield good results, but makes the representation of the document smaller and the clustering faster.