A new proposal Classification method based on Fuzzy Association Rule Mining for Student Academic Performance Prediction

Abstract

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