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 prediction progress. Indeed, a modification tree structure of a FP-growth tree is used in fuzzy frequent itemset mining, when a new requirement raised, the proposed algorithm mines directly in the tree structure for the best prediction result. | VNU Journal of Science: Policy and Management Studies, Vol. 33, No. 2 (2017) 104-113 A New Proposal Classification Method Based on Fuzzy Association Rule Mining for Student Academic Performance Prediction Cu Nguyen Giap*, Doan Thi Khanh Linh Vietnam University of Commerce, 79 Ho Tung Mau, Cau Giay, Hanoi,Vietnam Received 15 April 2017 Revised 10 June 2017, Accepted 28 June 2017 Abstract: Predicting student academic performance (SAPP) is an important issue in modern education system. Proper prediction of student performance improves construction of education principle in universities and helps students select and pursue suitable occupation. The predictions approaching fuzzy association rules (FAR) give advantages in this circumtance because it give the clear data-driven rules for prediction outcome. Applying fuzzy concept brings the linguistic terms that is close to people thought over a quantitative dataset, however an efficient mining mechanism of FAR require a high computing effort normally. The existing FAR-based algorithms for SAPP often use Apriori-based method for extracting fuzzy association rules, therefor they generate a huge number of candidates of fuzzy frequent itemsets and many 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 prediction progress. Indeed, a modification tree structure of a FP-growth tree is used in fuzzy frequent itemset mining, when a new requirement raised, the proposed algorithm mines directly in the tree structure for the best prediction result. The proposal model does not require to pre-determine the actecedent 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. Keywords: Classification, fuzzy, fuzzy association rule, student academic performance prediction. 1. Introdution performance of
