An associative rule-based classifier for Arabic medical text Online publication date: Wed, 06-Jan-2016
by Qasem A. Al-Radaideh; Samya S. Al-Khateeb
International Journal of Knowledge Engineering and Data Mining (IJKEDM), Vol. 3, No. 3/4, 2015
Abstract: Text classification is one of the methods used for managing, organising and retrieving the needed data among the huge available text. Several methods have been proposed to manipulate the text classification problem. In recent years, some studies proposed the use of Associative Classification (AC) approach. This paper examines an associative classification approach for the categorisation of text typed in Arabic language and related to medical domain. The approach discovers a set of association rules to build a classification model where three steps were applied to build the model: generating association rules, rule ordering and pruning, and then validation. The results of the experiments showed that the ordered decision list approach outperforms other approaches with accuracy reaching 90.6%. In general, the results of the experiments showed that association rule mining is a suitable method for building good classification models to categorise Arabic medical text.
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