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Table 4 Summary of used ACM methods

From: Fast rule-based bioactivity prediction using associative classification mining

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.