A Review of Data Mining Techniques in the Development of Decision Support Systems

Abstract

This study aims to examine the role and effectiveness of various data mining techniques in improving the performance of Decision Support Systems (DSS). Using a systematic literature review method, relevant academic papers and recent studies from the last decade were analyzed to identify common approaches, applications, and challenges. The findings show that classification, clustering, association rule mining, and anomaly detection are the most widely adopted data mining techniques in DSS development. Machine learning methods such as decision trees, neural networks, and support vector machines further contribute in improving prediction accuracy and decision quality. This discussion highlights that although data mining significantly strengthens the analytical capabilities of DSS, challenges such as data quality, model interpretability, and computational complexity remain important issues. Overall, this review underscores the importance of integrating advanced data mining approaches into DSS frameworks to support smarter, scalable and adaptable decision-making processes