Abstract:
Clustering is an unsupervised learning algorithm used to partition data and has wide
application in machine learning and other data studies. This research seeks to address the
issue of over-exhaustiveness within K-Medoids algorithm by proposing a new method for
selecting medoids which improves the efficiency of the created clustered and also reduce the
number of iterations. Further, this research incorporates an objective to identify the best
optimal value of k by using Silhouette Score which further improves the clustering accuracy
and reliability. We employed different datasets to execute eight different algorithms, which
included K-Means, K-Means++, Min-Max, K-Medoids, PAM, CLARA, CLARANS, and
Optimized K-Medoid to quantifying clustering performance through metrics such as optimal
clusters, iterations, and silhouette scores. The performance of the algorithms on clustering
was assessed based on the optimal value of clusters; iterations performed, and silhouette
scores. This proved that the Optimized K-Medoid offered the highest silhouette scores in the
least iterations. Hence, this study shows that the Optimized K-Medoid Clustering algorithm is
rather efficient than the other seven algorithms especially for clustering that require less time.
In general due to its robust nature, it may be suitable for use in other fields of data analysis.
