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 1816-949x

[img] Text
AJ 2019 (215).pdf
Restricted to Registered users only

Download (1MB) | Request a copy


Fuzzy Inference System (FIS) is a process of mapping input into the desired output using fuzzy logic theory where decisions can be made or patterns are discerned. This study aims to discuss on how non-fuzzy clustering output can be used to construct a model of FIS. Here, the proposed idea is to show the efficient use of the FIS as a prediction model for the data classification. In this study, employment income, self-employment income, property and transfer 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 which the best fit prediction model to the data is determined. In conclusion, the best prediction model in identifying income class is discovered based on the Root Mean Square Error (RMSE) value computed.

Item Type: Article
Uncontrolled Keywords: Household income data; fuzzy inference system; k-means clustering; root mean square; prediction model; Root Mean Square Error (RMSE)
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Applied Science and Technology > Department of Mathematics and Statistics
Depositing User: Miss Afiqah Faiqah Mohd Hafiz
Date Deposited: 24 Nov 2021 04:49
Last Modified: 24 Nov 2021 04:49
URI: http://eprints.uthm.edu.my/id/eprint/4076

Actions (login required)

View Item View Item