Optimizing Association Rule Mining Using Walk Back Artificial Bee Colony (WalkBackABC) Algorithm

Abstract

Association rule mining is considered to be the major task in data mining by most of the researchers where the process will find interesting relationships among various items in itemsets of huge database or a dataset. In this process, Aproiri algorithm is considered to be the familiar algorithm for performing association rule mining for implementing frequent itemset generation by providing minimum threshold value and we have explored advantages and disadvantages of association rule mining. In this paper, we have proposed WalkBackABC framework that optimizes ARM by increasing the exploration area and optimizes association rules which is further compared individually with apriori, FP growth and ABC algorithms in our proposed results where the rules generated are simple and comprehensible.