Rule Mining Framework for Students Performance Evaluation
P. Ajith1, B. Tejaswi2, M.S.S.Sai3
1P. Ajith, Research Scholar, KL University, Associate Prof. SRIJI College, Ongole, Andhra Pradesh, India.
2B. Tejaswi, Asst. Prof. Department of CSE,KKR & KSR Institute of Technology & Sciences, Guntur, Andhra Pradesh, India.
3M.S.S.Sai, Professor. Department of CSE, KKR & KSR Institute of Technology & Sciences, Guntur, Andhra Pradesh, India.
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 201-206 | Volume-2, Issue-6, January 2013. | Retrieval Number: F1157112612/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Academic Data Mining used many techniques such as Decision Trees, Neural Networks, Naïve Bayes, K- Nearest neighbor, and many others. Using these techniques many kinds of knowledge can be discovered such as association rules, classifications and clustering. The discovered knowledge can be used for prediction and analysis purposes of student patterns. Prior approaches used decision tree classifications optimized with ID3 algorithms to obtain such patterns. Among sets of items in transaction databases, Association Rules aims at discovering implicative tendencies that can be valuable information for the decision-maker which is absent in tree based classifications. So we propose a new interactive approach to prune and filter discovered rules. First, we propose to integrate user knowledge in the post processing task. Second, we propose a Rule Schema formalism extending the specifications to obtain association rules from knowledge base. Furthermore, an interactive framework is designed to assist the user throughout the analyzing task. Applying our new approach to discover the likelihood of students deviations / requiring special attention is organized and efficient providing more insight by considering more information. Compared to tree based classifications the results are better to understand and can be applied to real time use. An implementation of the proposed system validates our claim.
Keywords: Association Rules, Knowledge base, Prediction, and Rule Schema.