BZ-optimized K-Medoids cluster: A hybrid model for scalable and adaptive big data segmentation
TL;DR
A BZ-Optimized K-Medoids Clustering Algorithm that integrates fuzzy membership modelling, BZ-based medoid optimization, and distributed processing within a unified clustering framework is proposed that achieved superior clustering quality while maintaining scalability for large and heterogeneous Big Data environments.
Abstract
The increasing volume, diversity, and complexity of Big Data require clustering techniques capable of handling high-dimensional, noisy, and heterogeneous datasets while maintaining scalability and robustness. This study proposes a BZ-Optimized K-Medoids Clustering Algorithm (BZ-KMedoids) that integrates fuzzy membership modelling, BZ-based medoid optimization, and distributed processing within a unified clustering framework. Fuzzy membership enables partial assignment of data points to multiple clusters, improving the representation of overlapping cluster boundaries and uncertain data regions. BZ optimization refines medoid selection using membership information, enhancing cluster compactness, stability, and resistance to outliers. Distributed execution further supports efficient large-scale processing through parallel computation across multiple nodes. The proposed framework addresses key limitations of conventional clustering approaches, including sensitivity to initialization, rigid cluster assignments, and reduced effectiveness in complex data environments. Experimental evaluation was conducted using the Bank Marketing dataset and compared with K-Means and Agglomerative clustering methods. The proposed approach achieved a clustering error of 0.38, substantially lower than the values of 3.55 and 3.59 obtained by K-Means and Agglomerative clustering, respectively. BZ-KMedoids also achieved an accuracy of 0.99, outperforming K-Means (0.92) and Agglomerative clustering (0.91). Although the average convergence time was 12.35 s compared with 4.87 s for K-Means, the improved clustering quality, robustness, and scalability provide a favorable trade-off between efficiency and performance. The proposed framework achieved superior clustering quality while maintaining scalability for large and heterogeneous Big Data environments. These findings demonstrate the effectiveness of BZ-KMedoids for adaptive, scalable, and high-quality clustering in practical analytics applications.