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Fuzzy inference system model from non-fuzzy clustering output

Hamzah, Nur Atiqah and Kek, Sie Long (2019) Fuzzy inference system model from non-fuzzy clustering output. Journal of Engineering and Applied Sciences, 14 (12). pp. 4035-4042. ISSN 1816949X

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Abstract

Fuzzy Inference System (FIS) is a process of mapping input into the desired output using fuzzy logic theory where decisions can be made or pattern are dscerned. Ths study aims to discuss on how non-fuzzy clustering output can be used to constmct a model of FIS. Here, the proposed idea is to show the efficient use of the FIS as a predction model for the data classification. In this study, employment income, self-employment income, property and tramfer received are taken into account for clustering the household income data. Then, the FIS prediction model is built using the center values of clusters formed and the output of FIS is compared to the original cluster in whch the best fit predction model to the data is determined. In conclusion, the best predction model in identify~ngin come class is dscovered based on the Root Mean Square Error (RMSE) value computed.

Item Type: Article
Uncontrolled Keywords: Household income data, fuzzy inference system, k-meam clustering, root mean square, prediction model, Root Mean Square Error (RMSE)
Subjects: Q Science > QA Mathematics > QA101 Elementary mathematics. Arithmetic
Divisions: Faculty of Applied Science and Technology > Department of Mathematics and Statistic
Depositing User: Mr Abdul Rahim Mat Radzuan
Date Deposited: 31 Oct 2019 02:40
Last Modified: 31 Oct 2019 02:40
URI: http://eprints.uthm.edu.my/id/eprint/11844
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