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 , 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.
Highlights
Cluster analysis is the art of finding groups in datasets
The fuzzy linear regression was focused on the FLR model with the assumption of triangular fuzzy numbers (TFNs) being either symmetrical or asymmetrical, where they both represents by its own membership function
The second objective was achieved by means of existing methods which are multiple linear regression (MLR) model and fuzzy linear regression model that proposed by Tanaka, Ni and Zolfaghari
Summary
Cluster analysis is the art of finding groups in datasets. The classification of similar objects into groups is an important human activity. Partitional clustering algorithms aim to discover the groupings present in the data by optimising a specific objective function and iteratively improving the quality of the partitions. Performing hard assignments of points to clusters is not feasible in complex datasets where there are overlapping clusters To extract such overlapping structure, a FCM clustering algorithm could be used. Tanaka et al [5] is the first proposing a fuzzy linear regression model which is useful for certain systems and significant to fuzzy structure and human estimation. Fuzzy linear regression could be categorised into two types of situations based on the functional relationship; dependent (response) and independent (explanatory) variables. Zolfaghari et al [6] considered two factors parameter estimation of fuzzy linear regression model, known as the degree of fitting and the vagueness of the model, which can transfer into two approaches
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