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: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
GEOMATE
2019
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| Subjects: | |
| Online Access: | 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 http://umpir.ump.edu.my/id/eprint/25685/ |
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| Institution: | Universiti Malaysia Pahang |
| Language: | English English |
| Summary: | 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. |
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