International Journal of Advances in Intelligent Informatics
Vol 2, No 2 (2016): July 2016

Generated rules for AIDS and e-learning classifier using rough set approach

Sarina Sulaiman (UTM Big Data Centre, Universiti Teknologi Malaysia)
Nor Amalina Abdul Rahim (Faculty of Computing, Universiti Teknologi Malaysia)
Andri Pranolo ((Informatics Department, Universitas Ahmad Dahlan))

Article Info

Publish Date
31 Jul 2016


The emergence and growth of internet usage has accumulated an extensive amount of data. These data contain a wealth of undiscovered valuable information and problems of incomplete data set may lead to observation error. This research explored a technique to analyze data that transforms meaningless data to meaningful information. The work focused on Rough Set (RS) to deal with incomplete data and rules derivation. Rules with high and low left-hand-side (LHS) support value generated by RS were used as query statements to form a cluster of data. The model was tested on AIDS blog data set consisting of 146 bloggers and E-Learning@UTM (EL) log data set comprising 23105 URLs. 5-fold and 10-fold cross validation were used to split the data. Naïve algorithm and Boolean algorithm as discretization techniques and Johnson’s algorithm (Johnson) and Genetic algorithm (GA) as reduction techniques were employed to compare the results. 5-fold cross validation tended to suit AIDS data well while 10-fold cross validation was the best for EL data set. Johnson and GA yielded the same number of rules for both data sets. These findings are significant as evidence in terms of accuracy that was achieved using the proposed model

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Journal Info





Computer Science & IT


International journal of advances in intelligent informatics (IJAIN) e-ISSN: 2442-6571 is a peer reviewed open-access journal published three times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and ...