Enhancing the Apriori Algorithm for More Efficient Association Rule Mining

Abstract

The evaluation of transactional datasets is critical for organizations to optimize processes, uncover patterns, and enhance decision-making. This research focuses on designing a robust framework to evaluate the efficiency of transactional datasets by leveraging data mining techniques. The framework integrates methods such as association rule mining, clustering, and classification to assess data quality, identify redundant attributes, and improve insights. Results indicate that the proposed framework significantly enhances data interpretability and decision-making efficiency.