A Novel Association Rule Mining Model for Generating Positive and Negative Association Rules with Hybridized Meta-Heuristic Development

Abstract

Association rule mining (ARM) is to discover entire association rules with the confidence and support that are accordingly equivalent or above user-specific lower confidence and support. The existing suggested ARM methods can do the operations on the static datasets only, where the operation of the complete dataset must be accessible in the initial process of mining. Classical ARM methods are designed well on working with static data, and so, they cannot be used instantaneously for mining the association rules in data. To accomplish this, a novel framework is developed for association rule mining with the aid of an adaptive concept. Initially, the required data is collected from the benchmark datasets. The next step is to develop the novel association rule mining algorithm (NARMA), where the parameters are tuned by Modified Scavenger rate-based Dingo Billiards Optimization (MCR-DBO) by the hybridization of Dingo Optimizer (DOX) and Billiards-Inspired Optimization (BIO). This algorithm is used to identify the distinct itemsets of data. Consequently, the resultant dataset is given for finding the most eminent factors, where the itemsets are categorized into positive association rules as well as negative association rules. When the minimal threshold values are fixed with a minimum support count of 0.3, the proposed model achieved an average 81% confidence for generated top 1000 association rules from WebDocs dataset by minimizing the execution time and memory requirements. The outcomes disclose that the designed approach competently mines both the positive and negative rules and outperformed other recommended techniques