Association Rules Optimization using Artificial Bee Colony Algorithm with Mutation

Abstract

In data mining, Association rule mining is one of the popular and simple method to find the frequent item sets from a large dataset. While generating frequent item sets from a large dataset using association rule mining, computer takes too much time. This can be improved by using artificial bee colony algorithm (ABC). The Artificial bee colony algorithm is an optimization algorithm based on the foraging behavior of artificial honey bees. In this paper, artificial bee colony algorithm with mutation operator is used to generate high quality association rules for finding frequent item sets from large data sets. The mutation operator is used after the scout bee phase in this work. In general the rule generated by association rule mining technique do not consider the negative occurrences of attributes in them, but by using artificial bee colony algorithm (ABC) over these rules the system can predict the rules which contains negative attributes.

Multilevel Association Rule Mining for Large Datasets: A Review

“…For modifying sensitive items, frequent item-sets sensitive data are selected and then Artificial Bee Colony Optimization algorithm is used. Authors in [23] also proposed Artificial Bee Colony Algorithm with one additional operator called crossover for enhancing association rule quality. The crossover operator is used for better exploration ability, as this will generate more number of candidate solutions.…”

Association Rule Mining Based on Hybrid Whale Optimization Algorithm

“…Danish et al (2019) proposed a global ABC (GABCS) for data clustering. Sharma et al (2015) used ABC to generate high-quality association rules for searching frequent itemsets from large data sets. Cuckoo Search Algorithm (CS) had superior performance in exploring solution space to find the global optimal solution (Cuong- Le et al, 2021).…”