Comparative analysis between Euclidean distance metric andMahalanobis Distance Metric

Abstract

This research article presents a comparative analysis between the Euclidean distance metric and the Mahalanobis distance metric, two widely used measures in data analysis and pattern recognition. The primary objective of this study is to examine the performance differences between these metrics and provide insights into their respective strengths and weaknesses. Methodologically, we employ a systematic approach to evaluate the efficacy of both distance metrics using a diverse range of datasets. Key findings from our analysis highlight distinct behaviours of the Euclidean and Mahalanobis distance metrics in various contexts, shedding light on their applicability and limitations. The implications of these findings are significant for researchers and practitioners in fields such as machine learning, clustering, and classification, guiding the selection of appropriate distance metrics based on specific data characteristics. Overall, this research contributes to a deeper understanding of distance metrics’ impact on data analysis, paving the way for more informed decision-making in real-world applications.