Space constrained fast association rule mining with optimal support and confidence threshold using grammatical evolution: an effective nudge in policymaking

In the world of big data and social-media-headed governance and policymaking, data analysis is judged based on the speed and accuracy of execution. This study attempts to modify the existing Association Rule Mining (ARM) techniques by improving the space constraints. Although most of the ARM research is primarily focused on computational efficiency, it has not considered the identification of either the optimal support or the confidence value. Selection of ideal support, as well as confidence value, is vital for the ‘ARM’s quality. However, with the large dataset availability, the space vector poses the latest challenge in processing. Identification of the optimal parameters adapted to the space model is non-deterministic in nature. This research will focus on a Grammatical Evolution (GE) Association Rule Miner (GE-ARM) to identify the optimal threshold parameters for mining quality rules. Simulations are done using the FoodMart2000 dataset, and then, the proposed method is compared against the Apriori, the Frequent Pattern (FP) growth, and the Genetic Algorithms (GA). Simulation results exhibit substantial enhancements in space and rules generated together with time complexity. Compared to Apriori and FP-tree methods, the proposed GE-ARM achieves lesser runtime by around 20%. Such an improvisation would categorically change the dynamics of social media analytics by reducing the space constraints and can have more significant ramifications in policymaking. Therefore, such an improvement is undoubtedly an effective nudge in policymaking.