Discovery of Association Rules from University Admission System Data

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Abstract —Association rules discovery is one of the vital data mining techniques. Currently there is an increasing interest in data mining and educational systems, making educational data mining (EDM) as a new growing research community. In this paper, we present a model for association rules discovery from King Abdulaziz University (KAU) admission system data. The main objective is to extract the rules and relations between admission system attributes for better analysis. The model utilizes an apriori algorithm for association rule mining. Detailed analysis and interpretation of the experimental results is presented with respect to admission office perspective. Index Terms —Educational Data Mining, Association Rules Discovery, University Admission System. I. I NTRODUCTION Data mining aims at the discovery of useful information from large collection of data. Recently, there are increasing research interests in using data mining in education. This newly emerging field, called Educational Data Mining (EDM), concerns with developing methods that discover knowledge from data originating from educational environments [1]. Data mining techniques can discover useful information that can be used in formative evaluation to assist educators establish a pedagogical basis for decisions when designing or modifying an environment or teaching approach. The application of data mining in educational systems is an iterative cycle of hypothesis formation, testing, and refinement. As we can see in Fig. 1, educators and academics responsible are in charge of designing, planning, building and maintaining the educational systems. Different data mining techniques can be applied in order to discover useful knowledge that helps to improve both the academic and management processes [2]. Association rule mining is one of the major data mining techniques that interested in finding strong relationships and correlation among items in transactional databases. It can be employed in many areas including market analysis, decision support systems and financial forecast. An association rule has two measures: support and confidence that represent its statistical significance [3]. The problem of mining association rule is to discover the implication relation among items such that the presence of some items implies the presence of other items in the same transaction. Figure 1. Data Mining Cycle in Educational System [2]Mainly, all the proposed algorithms for association rules mining deals with transactional databases or market-basket data. These algorithms do not support relational databases naturally. To apply the same concepts and algorithms, relational database has to be converted to the transactional representation [4]. This requires the application of tedious conversion processes on large quantities of data before such algorithms can be applied as discussed with more details later in the paper.