Quantum based Whale Optimization Algorithm for Association Rule Mining in Static and Dynamic Stream Data

Abstract

The dynamic data stream is deal with a significant issue in various data mining applications. To deal with most recent information of the partial data, Whale optimization algorithm (WOA) based a sliding window based approach was proposed. WOA describes the explorative and exploitative phases that emulate the hunting behavior of the whales. This paper explains about the whale optimization algorithm and their abilities to discover the resources can be utilized for mining the association rules in static and dynamic data streams. Hence we propose quantum whale optimization algorithm to create the large itemsets in a dynamic transaction dataset employing the principle of WOA. We examine the time complexities method in dynamic association rule mining utilising QWOA for association rule optimization (QWARO). This algorithm is applied to dynamic situations by transforming static into dynamic data through the stream model named sliding window. The proposed approach QWARO achieves better results when compared with other algorithms namely BaCRO-II, WOA and GA.