K-Means cluster analysis in earthquake epicenter clustering

(1) * Pepi Novianti Mail (Department of Statistics, Faculty of Mathematics and Natural Science, University of Bengkulu, Indonesia, Indonesia)
(2) Dyah Setyorini Mail (Department of Statistics, Faculty of Mathematics and Natural Science, University of Bengkulu, Indonesia, Indonesia)
(3) Ulfasari Rafflesia Mail (Department of Mathematics, Faculty of Mathematics and Natural Science, University of Bengkulu, Indonesia)
*corresponding author


Bengkulu Province, Indonesia, which lies in two active faults, Semangko fault and Mentawai fault, is an area that has high seismic activity. As earthquake-prone area, the characteristic of each earthquake in Bengkulu Province needs to be studied. This paper presents the earthquake epicenter clustering in Bengkulu Province. Tectonic earthquake data at Bengkulu Province and surrounding areas from January 1970 to December 2015 are used. The data is taken from single-station Agency Meteorology, Climatology and Geophysics (BMKG) Kepahiang Bengkulu. K-Means clustering using Euclidean distance method is used in this analysis. The variables are latitude, longitude and magnitude. The optimum number of cluster is determined using Krzanowski and Lai (KL) index which is 7. The analysis for each clustering experiment with variation number of cluster is presented.


Cluster Analysis; K-Means; KL Index; Seismic Activity; Earthquake; Bengkulu Province; Indonesia




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