ENHANCING CLUSTERING PERFORMANCE: A HYBRID GENERALIZED K-MEANS APPROACH

This study developed a hybrid Generalized K means clustering algorithm to boostclustering accuracy, robustness and computational efficiency across diverse datasets. The proposed method integrates multiple clustering techniques, including Forgy, Lloyd, MacQueen, Hartigan and Wong, Likas and Faber, improving initialization, assignment, and updating processes. Advanced distance metrics, particularly the Mahalanobis distance, are incorporated to account for variable correlations and variances, ensuring precise cluster assignments. The algorithm's effectiveness is validated using datasets from the World Bank Commodity Price Publication 2022 and the R console repository, including Edgar Anderson's Iris data set