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. |