Effect of ISRI Stemming on Similarity Measure for Arabic Document Clustering

Arabic Document Clustering has increasingly become an important task for obtaining good results with the unsupervised learning task. This paper aims to evaluate the impact of the five measures (Cosine similarity, Jaccard coefficient, Pearson correlation, Euclidean distance and Averaged Kullback- Leibler Divergence) for Document Clustering with two types of pre-processing morphology-based The Information Science Research Institute (ISRI) is equivalent to the root-based stemmer and light stemmer; and without stemming without morphology) for an Arabic dataset. Stemming is known as a computational process used to reduce words to their stems. For classification, it is categorised as a recall-enhancing or precision-enhancing component. It is concluded that the method of ISRI for words is proved to be better than without stemming methods which use a five similarities/distance measures for Document Clustering.