@article{Nhita_2017, title={Comparative Study between Parallel K-Means and Parallel K-Medoids with Message Passing Interface (MPI)}, volume={2}, url={https://socj.telkomuniversity.ac.id/ojs/index.php/ijoict/article/view/86}, DOI={10.21108/IJOICT.2016.22.86}, abstractNote={<p>Data mining is a combination technology for analyze a useful information from dataset using some technique such as classification, clustering, and etc. Clustering is one of the most used data mining technique these day. K-Means and K-Medoids is one of clustering algorithms that mostly used because itâ€™s easy implementation, efficient, and also present good results. Besides mining important information, the needs of time spent when mining data is also a concern in today era considering the real world applications produce huge volume of data. This research analyzed the result from K-Means and K-Medoids algorithm and time performance using High Performance Computing (HPC) Cluster to parallelize K-Means and K-Medoids algorithms and using Message Passing Interface (MPI) library. The results shown that K-Means algorithm gives smaller SSE than K-Medoids. And also parallel algorithm that used MPI gives faster computation time than sequential algorithm.</p>}, number={2}, journal={International Journal on Information and Communication Technology (IJoICT)}, author={Nhita, Fhira}, year={2017}, month={Jul.}, pages={27} }