Improving Retrieval Performance of Case Based Reasoning Systems

This technical report presents a novel approach to enhance the retrieval performance of Case Based Reasoning (CBR) systems by integrating fuzzy clustering techniques, specifically Fuzzy C-Means (FCM) and K-Means, into the CBR cycle. The objective is to address the limitations of traditional CBR methodologies, particularly in the medical domain, where the complexity and volume of data can hinder effective case retrieval. The report outlines the challenges faced in CBR, such as the representation of medical cases and the computational inefficiencies during the retrieval phase. It details how the proposed integration of clustering techniques can streamline the retrieval process, reduce the search space, and improve the overall efficiency of CBR systems. The approach is validated using a publicly available immunotherapy dataset, demonstrating that the incorporation of FCM significantly enhances retrieval speed and accuracy. The findings suggest that this method could be beneficial for decision support systems in various medical applications.