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A Comparison study of DBScan and K-Means Clustering on the Tropical Rainfall Measuring Mission (TRMM) 1998-2008
Geraldi Catur Pamuji; Rongtao Hou

Postgraduate Faculty, Universitas Komputer Indonesia, Bandung, Indonesia.

School of Computer and Software, Nanjing University of Information Science and Technology, Jiangsu, China.

Abstract

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. K-means and DBScan are methods for cluster analysis in data mining. In this paper, rainfall data in Bandung and Jakarta based on TRMM will be analyzed and compared in efficiency and accuracy using each algorithm.

Keywords: Data Mining, K-Means, DBScan, Clustering, TRMM

Topic: Informatic and Information System

Link: https://ifory.id/abstract/98YteHrQ3F4f

Conference: 2nd International Conference on Informatics, Engineering, Science and Technology (INCITEST 2019)

Plain Format | Corresponding Author (Geraldi Catur Pamuji)

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