Clustering of rainfall data using k-means algorithm

Mohd Sham, Mohamad and Yuhani, Yusof and Ku Muhammad Na’im, Ku Khalif and Mohd Khairul Bazli, Mohd Aziz (2019) Clustering of rainfall data using k-means algorithm. In: The Ninth International Conference on Geotechnique, Construction Materials and Environment (GEOMATE 2019) , 20-22 November 2019 , Tokyo, Japan. pp. 1-8.. ISBN 978-4-909106025 C3051

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Abstract

Clustering algorithms in data mining is the method for extracting useful information for a given data. It can precisely analyze the volume of data produced by modern applications. The main goal of clustering is to categorize data into clusters according to similarities, traits and behavior. This study aims to describe regional cluster pattern of rainfall based on maximum daily rainfall in Johor, Malaysia. K-Means algorithm is used to obtain optimal rainfall clusters. This clustering is expected to serve as an analysis tool for a decision making to assist hydrologist in the water research problem.

Item Type:	Conference or Workshop Item (Lecture)
Uncontrolled Keywords:	Clustering; K-Means; Rainfall
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General)
Faculty/Division:	Faculty of Industrial Sciences And Technology
Depositing User:	Pn. Hazlinda Abd Rahman
Date Deposited:	17 Dec 2019 02:26
Last Modified:	23 Dec 2019 07:42
URI:	http://umpir.ump.edu.my/id/eprint/25685
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