Diversification Heuristics in Bees Swarm Optimization for Association Rules Mining

Abstract

Association rules mining is becoming more challenging with the large transactional databases typical of modern times. Conventional exact algorithms for association rules mining struggle to cope with very large databases, especially in terms of run-time performance. To address this problem, several evolutionary and swarm intelligence-based approaches have been proposed. One of these is HBSO-TS, which is a hybrid approach combining Bees Swarm Optimization with Tabu Search and has been shown to outperform other state-of-the art bio-inspired approaches. The main drawback of HBSO-TS is that while the intensification is improved using Tabu Search, the diversification remains unchanged compared to BSO-ARM, i.e., the first approach proposed in the literature using Bees Swarm Optimization for association rules mining. To ensure a better balance between intensification and diversification, this paper proposes two new heuristics for determining the search area of the bees. We conducted experimental evaluation on well known data instances to show that both heuristics improve the performance of HBSO-TS. Moreover, we show the usefulness of our heuristics in the special case of mining association rules from diversified data, as in the case of Weblog mining.