Association Rule Mining Based on Hybrid Whale Optimization Algorithm

Abstract

Association Rule Mining(ARM) is one of the most significant and active research areas in data mining. Recently, Whale Optimization Algorithm (WOA) has been successfully applied in the field of data mining, however, it easily falls into the local optimum. Therefore, an improved WOA based adaptive parameter strategy and Levy Flight mechanism (LWOA) is applied to mine association rules. Meanwhile, a hybrid strategy that blends two algorithms to balance the exploration and exploitation phases is put forward, that is, grey wolf optimization algorithm (GWO), artificial bee colony algorithm (ABC) and cuckoo search algorithm (CS) are devoted to improving the convergence of LWOA. The approach performs a global search and finds the association rules sets by modeling the rule mining task as a multi-objective problem that simultaneously meets support, confidence, lift, and certain factor, which is examined on multiple data sets. Experimental results verify that the proposed method has better mining performance compared to other algorithms involved in the paper.

Research and Application of an Improved Sparrow Search Algorithm

“…However, this method only starts from a single-objective perspective and lacks comprehensive consideration. Ye Z et al [27] combined different intelligent optimization algorithms based on the whale optimization algorithm into a hybrid whale algorithm and linearly weighted the support, confidence, and accuracy factors into a new adaptive function in order to pursue a new optimization function that is based on the whale optimization algorithm. A new fitness function in pursuit of better rule quality: Linear weighting of different objective functions is a simple and effective way, but it is difficult to determine the appropriate value for the setting of weights, which requires a certain amount of a priori knowledge of the decision maker.…”