Parameter setting procedure via quick parameter evaluation in frequent pattern mining for outbreak detection

Data sources for outbreak detection nowadays not only focus on emergency department or hospital-based data but also grocery data. However, the size of huge data, may consume higher time and extreme number of discovered pattern. Unfortunately not all the discovered pattern from the frequent mining is interesting pattern. Hence frequent pattern mining algorithms producing numbers of frequent pattern, still parameter uses in minimum support and which frequent itemset producing better pattern remains fairly open. It is important to gains some limitation of minimum support to be applied to the frequent mining algorithm so that we not end up at compiling higher patterns including a normal pattern. We propose a procedure based on quick parameter setting to estimate minimum support and also frequent itemset. Our empirical validation shown the procedure will extract ranging minimum support and frequent itemset to be considered to generate interesting pattern.