A New Proposal Classification Method Based on Fuzzy Association Rule Mining for Student Academic Performance Prediction 10

Abstract

Predicting student academic performance (SAPP) is an important task in modern
education system. Proper prediction of student performance improves construction of education
principles in universities and helps students select and pursue suitable occupations. The
prediction approaching fuzzy association rules (FAR) give advantages in this circumstance
because it gives the clear data-driven rules for prediction outcome. Applying fuzzy concept
brings the linguistic terms that are close to people thought over a quantitative dataset, however
an efficient mining mechanism of FAR requires a high computing effort normally. The existing
FAR-based algorithms for SAPP often use Apriori-based method for extracting fuzzy association
rules, consequently they generate a huge number of candidates of fuzzy frequent itemsets and
various redundant rules. This paper presents a new proposal model of predictor using FAR to
elevating prediction performance and avoids extraction of the fixed set of FAR before
predictions progress. Indeed, a modification tree structure of a FP-growth tree is used in fuzzy
frequent itemset mining, when a new requirement rises, the proposed algorithm mines directly in
the tree structure for the best prediction results. The proposal model does not require to predetermine
the antecedents of prediction problem before the training phrase. It avoids searching
for non-relative rules and prunes the conflict rules easily by using a new rule relatedness
estimation.