A Performance of the Scattered Averaging Technique based on the Dataset for the Cluster Center Initialization
Arief Bramanto Wicaksono Putra, Achmad Fanany Onnilita Gaffar, Bedi Suprapty, Mulyanto Mulyanto · International Journal of Modern Education and Computer Science · 2021
Clustering is one of the primary functions in data mining explorations and statistical data analysis which widely used in various fields.There are two types of the clustering algorithms which try to optimize certain objective function, i.e. the hierarchical and partitional clustering.This study focuses on the achievement of the best cluster results of the hard and soft clustering (K-Mean, FCM, and SOM clustering).The validation index called GOS (Global Optimum Solution) used to evaluate the cluster results.GOS index defined as a ratio of the distance variance within a cluster to the distance variance between clusters.The aim of this study is to produce the best GOS index through the use of the proposed method called the scattered averaging technique based on datasets for the cluster center initialization.The cluster results of each algorithm are also compared to determine the best GOS index between them.By using the annual rainfall data as the dataset, the results of this study showed that the proposed method significantly improved K-Mean clustering ability to achieve the global optimum solution with a performance ratio of 69.05% of the total performance of the three algorithms.The next best clustering algorithm is SOM clustering (24.65%) followed by FCM clustering (6.30%).In addition, the results of this study also showed that the three clustering algorithms achieve their best global optimum solution at the number of even clusters.