Table 4.

Summary of used ACM methods

Method	Summary
CBA	Classification based on association rules [11,45] first discovers all rules by using Apriori approach, and then prunes rules by database coverage technique.
CPAR	Classification based on predictive association rules [25] uses a greedy approach—a weighted version of FOIL-gain to identify features and discover rules. A PNArray data structure is utilized to reduce storage space and computation time [13].
CMAR	Classification based on multiple association rules [12] employs FP-growth method to discover rules. FP-growth builds a FP-tree based on the dataset using less storage space and improves the efficiency of retrieving rules.