Comparative Analysis between K-Means and K-Medoids for Statistical Clustering

Clustering dynamic data is a challenge inidentifying and forming groups. This unsupervised learningusually leads to undirected knowledge discovery. The clusterdetection algorithm searches for clusters of data which aresimilar to one another by using similarity measures.Determining the suitable algorithm which can bring theoptimized groups cluster could be an issue. Depending on theparameters and attributes of the data, the results yielded fromusing both K-Means and K-Medoids could be varied. Thispaper presents a comparative analysis of both algorithms indifferent data clusters to lay out the strengths and weaknessesof both. Thorough studies were conducted in determining thecorrelation of the data with the algorithms to find therelationship among them.