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A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income

Ramly, Nurfarawahida and Rusiman, Mohd Saifullah and Che Him, Norziha and Nor, Maria Elena and S., Suparman and Ahmad Basri, NurAin Zafirah and Mohamad, Nazeera (2018) A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income. International Journal of Engineering & Technology, 7 (4.30). pp. 473-478. ISSN 2227524X

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Abstract

Analysis by human perception could not be solved using traditional method since uncertainty within the data have to be dealt with first. Thus, fuzzy structure system is considered. The objectives of this study are to determine suitable cluster by using fuzzy c-means (FCM) method, to apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression (FLR) as proposed by Tanaka and Ni and to improve the FCM method and FLR model proposed by Zolfaghari to predict manufacturing income. This study focused on FLR which is suitable for ambiguous data in modelling. Clustering is used to cluster or group the data according to its similarity where FCM is the best method. The performance of models will measure by using the mean square error (MSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE). Results shows that the improvisation of FCM method and FLR model obtained the lowest value of error measurement with MSE=1.825 11 10 , MAE=115932.702 and MAPE=95.0366. Therefore, as the conclusion, a new hybrid of FCM method and FLR model are the best model for predicting manufacturing income compared to the other models.

Item Type: Article
Uncontrolled Keywords: Fuzzy linear regression (FLR); fuzzy c-means (FCM); mean square error (MSE)
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Applied Science and Technology > Department of Mathematics and Statistic
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 31 Oct 2019 02:33
Last Modified: 31 Oct 2019 02:33
URI: http://eprints.uthm.edu.my/id/eprint/11788
Statistic Details: View Download Statistic

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