TY - JOUR
AU - Nhita, Fhira
PY - 2017/07/25
Y2 - 2021/02/27
TI - Comparative Study between Parallel K-Means and Parallel K-Medoids with Message Passing Interface (MPI)
JF - International Journal on Information and Communication Technology (IJoICT)
JA - ijoict
VL - 2
IS - 2
SE - Computational Science
DO - 10.21108/IJOICT.2016.22.86
UR - https://socj.telkomuniversity.ac.id/ojs/index.php/ijoict/article/view/86
SP - 27
AB - 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.
ER -