Whale Optimization Algorithm for association rule mining. Input: Number of Maximum Iteration and Population Size, Minsupport, minconfedence. Step 01: Initialize the population size for n search agents.[Xi(i=1,2,3,...n)] Step 02: Initialize i, A, C, L, and p. Step 03: Compute the fitness value of each search agent/whale. Step 04: X* = the best rule Step 05: While (CurrentIteration <= MaximumIteration ) Step 06: Update a, A, C, L and p. Step 07: For all whale poplation check if (p<0.5) if(|A|<1) For each Item in the solution Xi. Update Items. Else if(|A|=1) Select a random Item in Xi. Update Items. End if For each item in the solution Xi. If the Item is odd, it belongs to the antecedent, Otherwise, it belongs to the consequence. End for Step 08: Calculate the fitness of each search agent. Step 09: Update X* if there is a better solution. Step 11: Iteration = currentIteration + 1 Step 12: End While Step 13: Return X* 1
