A Data Mining Framework for Enhanced Evaluation of Transactional Data Efficiency

Abstract

Evaluating transactional datasets is crucial for organizations to optimize business processes, derive meaningful patterns, and improve decision-making. This research proposes an improved framework for assessing the efficiency of transactional datasets by utilizing advanced data mining techniques. The framework incorporates association rule mining, clustering, and classification to examine data quality, eliminate redundant attributes, and extract valuable insights. Findings suggest that this framework enhances data interpretability and significantly improves decision-making efficiency.