EVALUATING THE EFFECT OF STEMMING IN CLUSTERING OF ARABIC DOCUMENTS
Journal Title: Academic Research International - Year 2011, Vol 1, Issue 1
Abstract
In text mining, the concept of clustering is common and important to retrieve and categorizedocuments. Clustering techniques divers and many of them are applied on different languages butnot on Arabic. K-means algorithm is a widely used clustering technique that seeks to minimize theaverage squared distance between points in the same cluster. This paper aimed to implement andevaluate the K-means algorithm on clustering of Arabic documents and estimate the effect ofstemming on such clustering algorithm. The experimented work showed that the accuracy ofclustering Arabic documents using the K-means algorithm varies from low to very good. The bestachieved result was 69% of successful documents without stemming. Furthermore, the effect ofstemming resulted in decreasing the accuracy of retrieving documents because the stemming is anabstract of a word which leads to miss-discriminating of documents. The best result scored withstemming was 55% of successful documents, when applying the same thresholds.
Authors and Affiliations
Omaia Al-Omari
Keywords
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- EP ID EP113711
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