A Rough Set Theory Approach for Rule Generation and Validation Using RSES

A Rough Set Theory Approach for Rule Generation and Validation Using RSES

Hemant Rana (School of Computer and Information Sciences, Indira Gandhi National Open University, New Delhi, India) and Manohar Lal (School of Computer and Information Sciences, Indira Gandhi National Open University, New Delhi, India)
Copyright: © 2016 |Pages: 16
DOI: 10.4018/IJRSDA.2016010104
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Abstract

Despite significant progress in e-learning technology over previous years, in view of huge sizes of data and databases, efficient knowledge extraction techniques are still required to make e-learning effective tool for delivery of learning. Rough set theory approach provides an effective technique for extraction of knowledge out of massive data. In order to provide effective support to learners, it is essential to know individual style of learning for each learner. For determining learning style of each learner, one is required to extract essentials of style of learning from a large number of parameters including academic background, profession, time available etc. In such scenario, rough theory proves a useful tool. In this paper, a rough set theory approach is proposed for determining learning styles of learners efficiently, so that based on the style, a learner may be provided learning support on the basis of requirement of the learner. These is achieved by eliminating redundant and ambiguous data and by generating reduct set, core set and rules from the given data. The results of this study are validated through RSES software by using same rough set analysis.
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2. Literature Review

The rough set theory as a tool for analysis of voluminous data, which may be possibly incomplete and inconsistent, was introduced in 1982 by Zdzislaw Pawlak (Zhang, Li & Pan, 2012). It is a mathematical approach to handle and use imperfect knowledge for taking appropriate actions.

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