Authors: Qasem A. Al-Radaideh; Samya S. Al-Khateeb
Addresses: Department of Computer Information Systems, Faculty of Information Technology and Computer Science, Yarmouk University, Irbid, Jordan ' Department of Computer Science, Faculty of Preparatory Year, Northern Border University, Ara'a, Saudi Arabia
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.
Keywords: text mining; Arabic text classification; associative classification; medical texts; association rules; rule ordering; pruning; validation.
International Journal of Knowledge Engineering and Data Mining, 2015 Vol.3 No.3/4, pp.255 - 273
Available online: 05 Jan 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article