Rule Pruning Methods in Associative Classification Text Mining
Fadi Thabtah,Wael Hadi,Hussein Abdel-jaber,Mofleh Aldiabat
Journal of Intelligent Computing, Vol. 1, No. 1, 2010.
DOI: 
Link: http://dline.info/jic/fulltext/v1n1/1.pdf
Abstract:
Associative classification is the integration of classification and association rule discovery in data mining. This approach often derives higher quality classifiers with reference to classification accuracy than traditional classification approaches such as decision trees and rule induction. In this paper, the problem of rule pruning in associative text categorisation is investigated. We propose five rule pruning methods within an existing associative classification algorithm called MCAR. Experimental results against large text collection (Reuters-21578) using the developed pruning methods as well as other known existing methods (Database coverage, lazy pruning) are conducted. The bases of the experiments are the classification accuracy and the number of generated rules. The results derived show that the proposed rule pruning methods derive higher quality and more scalable classifiers than those produced by lazy and database coverage pruning approaches. In addition, the numbers of rules generated by the developed pruning methods are usually less than those of lazy and database coverage. This makes the associative classifiers generated when using the proposed methods more maintainable by end users.