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Document Clustering is a branch of a larger area of scientific study known asdata mining .which is an unsupervised classification using to find a structurein a collection of unlabeled data. The useful information in the documents canbe accompanied by a large amount of noise words when using Full TextRepresentation, and therefore will affect negatively the result of theclustering process. So it is with great need to eliminate the noise words andkeeping just the useful information in order to enhance the quality of theclustering results. This problem occurs with different degree for any languagesuch as English, European, Hindi, Chinese, and Arabic Language. To overcomethis problem, in this paper, we propose a new and efficient Keyphrasesextraction method based on the Suffix Tree data structure (KpST), the extractedKeyphrases are then used in the clustering process instead of Full TextRepresentation. The proposed method for Keyphrases extraction is languageindependent and therefore it may be applied to any language. In thisinvestigation, we are interested to deal with the Arabic language which is oneof the most complex languages. To evaluate our method, we conduct anexperimental study on Arabic Documents using the most popular Clusteringapproach of Hierarchical algorithms: Agglomerative Hierarchical algorithm withseven linkage techniques and a variety of distance functions and similaritymeasures to perform Arabic Document Clustering task. The obtained results showthat our method for extracting Keyphrases increases the quality of theclustering results. We propose also to study the effect of using the stemmingfor the testing dataset to cluster it with the same documents clusteringtechniques and similarity/distance measures.

A new keyphrases extraction method based on suffix tree data structure for arabic documents clustering