Stemming effectiveness in clustering of arabic documents

Abstract

Clustering is an important task gives good results with information retrieval (IR), it aims to automatically put similar documents in one cluster. Stemming is an important technique, used as feature selection to reduce many redundant features have the same root in root-based stemming and have the same syntacticalform in light stemming. Stemming has many advantages it reducesthe size of document and increases processing speed and used in many applications as information retrieval (IR). In this paper, we have evaluatedstemmingtechniques in clustering of Arabic language documents and determined the most efficient in preprocessing of Arabic language, whichis more complex than most other languages. Evaluation used three stemming techniques: root-based Stemming, light Stemming and without stemming. K-means, one of famous and widely clustering algorithm, is applied for clustering. Evaluation depends on recall, precision andF-measure methods. From experiments, results show that light stemming achieved best results in terms of recall, precision and F-measure when compared with others stemming.