Itemsets-Based Amharic Document Categorization Using an Extended A Priori Algorithm

Document categorization is gaining importance due to the large volume of electronic information which requires automatic organization and pattern identification. Due to the morphological complexity of the language, automatic categorization of Amharic documents has become a difficult talk to carry out. This paper presents a system that categorizes Amharic documents based on the frequency of itemsets obtained after analyzing the morphology of the language. We selected seven categories into which a given document is to be classified. The task of categorization is achieved by employing an extended version of a priori algorithm which had been traditionally used for the purpose of knowledge mining in the form of association rules. The system is tested with a corpus containing Amharic news documents and experimental results are reported.