Open AccessA Comparison study of DBScan and K-Means Clustering in Jakarta rainfall based on the Tropical Rainfall Measuring Mission (TRMM) 1998-2007
Author(s) -
Geraldi Catur Pamuji,
H Rongtao
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/879/1/012057
Subject(s) - dbscan , meteorology , cluster analysis , environmental science , cluster (spacecraft) , mesoscale meteorology , satellite , remote sensing , computer science , data mining , geography , engineering , machine learning , fuzzy clustering , canopy clustering algorithm , aerospace engineering , programming language
The purpose of this study is to compare between two different of cluster analysis algorithm in data mining on the Tropical Rainfall Measuring Mission (TRMM). The TRMM is a joint mission between NASA and the Japan Aerospace Exploration (JAXA) Agency to study rainfall for weather and climate research. The TRMM satellite data-sets used in this research is a 3-hourly rainfall data within 10 years from 1998 to 2007. These data-sets will be analyzed by two different cluster analysis algorithms in data mining which are K-means and DBScan. In this paper, rainfall data in Jakarta based on TRMM was analyzed and compared in the efficiency and the accuracy using each algorithm. The comparison results of the two algorithmic processes can be seen from several parameters, especially from the number of clusters formed and the time needed to process the model.