Malaysia household incomes classification prediction with k-means clustering and fuzzy inference system

The economy level of the citizen has become a main concern for Malaysia as a developing country to improve the living status. On this point of view, the household income data would be a very useful information to measure the economic status of the population in Malaysia. This study aims to build a c...

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Main Author:	Hamzah, Nur Atiqah
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	QA Mathematics
Online Access:	http://eprints.uthm.edu.my/20/1/24p%20NUR%20ATIQAH%20BINTI%20HAMZAH.pdf http://eprints.uthm.edu.my/20/2/NUR%20ATIQAH%20BINTI%20HAMZAH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/20/3/NUR%20ATIQAH%20BINTI%20HAMZAH%20WATERMARK.pdf
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spelling	my-uthm-ep.202021-06-06T08:26:38Z Malaysia household incomes classification prediction with k-means clustering and fuzzy inference system 2018-08 Hamzah, Nur Atiqah QA Mathematics The economy level of the citizen has become a main concern for Malaysia as a developing country to improve the living status. On this point of view, the household income data would be a very useful information to measure the economic status of the population in Malaysia. This study aims to build a classification prediction of household incomes using fuzzy inference system (FIS) from the K-means clustering outputs. Thus, this study focuses on three main objectives which are (a) To apply K-means clustering on household incomes data, (b) To propose the prediction of household incomes classification using FIS, and (c) To analyze and validate the classification solution for household incomes and to compare with discriminant analysis. Initially, the number of groups in the household income data is determined by using K-means clustering. Accordingly, the outputs from K-means clustering are used to identify the membership functions, namely, triangle, trapezoidal and Gaussian membership functions. Furthermore, FIS models for each membership function are built for the household income class prediction based on clustering outputs. For verification, the root mean square error (RMSE) value for each FIS model is calculated and the percentage of data correctly classified using the FIS models built is compared with the discriminant analysis output. As a result, it is found that Mamdani FIS model with Gaussian membership function is the best model with the RMSE is 1.0396, while the percentage of data correctly classified is 64.9989%. In conclusion, the classification prediction of household incomes discussed in this thesis could identify the predicted class of household income in a tractable way and the efficiency of the technique used in this thesis for classification prediction of household income is highly recommended. 2018-08 Thesis http://eprints.uthm.edu.my/20/ http://eprints.uthm.edu.my/20/1/24p%20NUR%20ATIQAH%20BINTI%20HAMZAH.pdf text en public http://eprints.uthm.edu.my/20/2/NUR%20ATIQAH%20BINTI%20HAMZAH%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/20/3/NUR%20ATIQAH%20BINTI%20HAMZAH%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Sains Gunaan dan Teknologi
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA Mathematics
spellingShingle	QA Mathematics Hamzah, Nur Atiqah Malaysia household incomes classification prediction with k-means clustering and fuzzy inference system
description	The economy level of the citizen has become a main concern for Malaysia as a developing country to improve the living status. On this point of view, the household income data would be a very useful information to measure the economic status of the population in Malaysia. This study aims to build a classification prediction of household incomes using fuzzy inference system (FIS) from the K-means clustering outputs. Thus, this study focuses on three main objectives which are (a) To apply K-means clustering on household incomes data, (b) To propose the prediction of household incomes classification using FIS, and (c) To analyze and validate the classification solution for household incomes and to compare with discriminant analysis. Initially, the number of groups in the household income data is determined by using K-means clustering. Accordingly, the outputs from K-means clustering are used to identify the membership functions, namely, triangle, trapezoidal and Gaussian membership functions. Furthermore, FIS models for each membership function are built for the household income class prediction based on clustering outputs. For verification, the root mean square error (RMSE) value for each FIS model is calculated and the percentage of data correctly classified using the FIS models built is compared with the discriminant analysis output. As a result, it is found that Mamdani FIS model with Gaussian membership function is the best model with the RMSE is 1.0396, while the percentage of data correctly classified is 64.9989%. In conclusion, the classification prediction of household incomes discussed in this thesis could identify the predicted class of household income in a tractable way and the efficiency of the technique used in this thesis for classification prediction of household income is highly recommended.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Hamzah, Nur Atiqah
author_facet	Hamzah, Nur Atiqah
author_sort	Hamzah, Nur Atiqah
title	Malaysia household incomes classification prediction with k-means clustering and fuzzy inference system
title_short	Malaysia household incomes classification prediction with k-means clustering and fuzzy inference system
title_full	Malaysia household incomes classification prediction with k-means clustering and fuzzy inference system
title_fullStr	Malaysia household incomes classification prediction with k-means clustering and fuzzy inference system
title_full_unstemmed	Malaysia household incomes classification prediction with k-means clustering and fuzzy inference system
title_sort	malaysia household incomes classification prediction with k-means clustering and fuzzy inference system
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Gunaan dan Teknologi
publishDate	2018
url	http://eprints.uthm.edu.my/20/1/24p%20NUR%20ATIQAH%20BINTI%20HAMZAH.pdf http://eprints.uthm.edu.my/20/2/NUR%20ATIQAH%20BINTI%20HAMZAH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/20/3/NUR%20ATIQAH%20BINTI%20HAMZAH%20WATERMARK.pdf
_version_	1747830509123141632

Malaysia household incomes classification prediction with k-means clustering and fuzzy inference system

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