Robust Mahalanobis K-Means Algorithm in Comparison With Other Existing Clustering Methods
This study enhances K-means Mahalanobis clustering using Density Power Divergence (DPD) for outlier handling and detection. Through the utilization of simulations and the analysis of real-world data, our approach consistently outperforms standard K-means, Mahalanobis K-means, Fuzzy C-means, and others in clustering datasets with outliers. While our method performs similarly to others on spherical datasets, it ranks second to DBSCAN for arbitrary shapes. We showcase its superiority on real-life datasets (Iris flower and wheat seed), demonstrating resilient outlier identification. By navigating various structures and cluster characteristics, our Modified Mahalanobis K-means method proves adaptable and robust, offering insights into diverse clustering scenarios. The study explores robust clustering to mitigate outlier impact in statistical analysis, contributing to improved clustering in outlier-prone datasets.
Tabi Serebour, Eleazer, "Robust Mahalanobis K-Means Algorithm in Comparison With Other Existing Clustering Methods" (2023). ETD Collection for University of Texas, El Paso. AAI30635900.