Clustering of rainfall data using k-means algorithm

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 stu...

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Main Authors:	Mohd Sham, Mohamad, Yuhani, Yusof, Ku Muhammad Na’im, Ku Khalif, Mohd Khairul Bazli, Mohd Aziz
Format:	Conference or Workshop Item
Language:	English English
Published:	GEOMATE 2019
Subjects:	Q Science (General) T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/25685/ http://umpir.ump.edu.my/id/eprint/25685/1/37.%20Clustering%20of%20rainfall%20data%20using%20k-means%20algorithm.pdf http://umpir.ump.edu.my/id/eprint/25685/2/37.1%20Clustering%20of%20rainfall%20data%20using%20k-means%20algorithm.pdf

id	ump-25685
recordtype	eprints
spelling	ump-256852019-12-23T07:42:52Z http://umpir.ump.edu.my/id/eprint/25685/ Clustering of rainfall data using k-means algorithm Mohd Sham, Mohamad Yuhani, Yusof Ku Muhammad Na’im, Ku Khalif Mohd Khairul Bazli, Mohd Aziz Q Science (General) T Technology (General) 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. GEOMATE 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25685/1/37.%20Clustering%20of%20rainfall%20data%20using%20k-means%20algorithm.pdf pdf en http://umpir.ump.edu.my/id/eprint/25685/2/37.1%20Clustering%20of%20rainfall%20data%20using%20k-means%20algorithm.pdf 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
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	Q Science (General) T Technology (General)
spellingShingle	Q Science (General) T Technology (General) Mohd Sham, Mohamad Yuhani, Yusof Ku Muhammad Na’im, Ku Khalif Mohd Khairul Bazli, Mohd Aziz Clustering of rainfall data using k-means algorithm
description	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.
format	Conference or Workshop Item
author	Mohd Sham, Mohamad Yuhani, Yusof Ku Muhammad Na’im, Ku Khalif Mohd Khairul Bazli, Mohd Aziz
author_facet	Mohd Sham, Mohamad Yuhani, Yusof Ku Muhammad Na’im, Ku Khalif Mohd Khairul Bazli, Mohd Aziz
author_sort	Mohd Sham, Mohamad
title	Clustering of rainfall data using k-means algorithm
title_short	Clustering of rainfall data using k-means algorithm
title_full	Clustering of rainfall data using k-means algorithm
title_fullStr	Clustering of rainfall data using k-means algorithm
title_full_unstemmed	Clustering of rainfall data using k-means algorithm
title_sort	clustering of rainfall data using k-means algorithm
publisher	GEOMATE
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25685/ http://umpir.ump.edu.my/id/eprint/25685/1/37.%20Clustering%20of%20rainfall%20data%20using%20k-means%20algorithm.pdf http://umpir.ump.edu.my/id/eprint/25685/2/37.1%20Clustering%20of%20rainfall%20data%20using%20k-means%20algorithm.pdf
first_indexed	2023-09-18T22:39:35Z
last_indexed	2023-09-18T22:39:35Z
_version_	1777416825027428352

Clustering of rainfall data using k-means algorithm

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