Optimising Clustering Accuracy Using Mahalanobis Distance-Based Ensemble Methods - A Novel Data Analysis Paradigm

A conspicuous paradigm in Machine Learning involves leveraging the collaborative integration of multiple models through Ensemble Learning to surpass the performance, accuracy, and generalization capabilities of individual procedures. This study aims to investigate the deployment of Ensemble Learning as an approach to refine the precision of Cluster Analysis. Conventional clustering algorithms normally tackle with the complexity of datasets portrayed by multifaceted attributes, many of which demonstrate obscure or non-linear interdependencies. By employing advanced Ensemble Learning approaches, this research hopes to elaborate clustering efficacy and perceive latent patterns that remain imperceptible through traditional approaches. The proposed investigation underscores Ensemble Learning as a transformative approach in the domain of cluster analysis within data science. Its ability to augment the accurateness of clustering techniques and extract hidden structures from data not promptly apparent through conventional algorithms accentuates its indispensability. Accordingly, this research endeavours to revolutionize the precision and applicability of clustering methodologies in solving real-world data analysis challenges. To substantiate the efficacy of the proposed framework, a comprehensive comparative evaluation is performed, benchmarking its outcomes against those obtained from varied datasets and established clustering algorithms.