Fig. 16
From: A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks
The subtractive clustering MSE. There are some clustering methods like K-Means, FCM, and subtractive. To find which one is the best for our research, we have clustered the customers using K-Means, FCM, and subtractive clustering methods and compared the methods’ MSEs. The MSEs (Mean-Square Error) of the subtractive is shown in Fig. 16. The error mean is = 2.9142e-0.8 and std. = 5.5786e-0.5 The Mean squared error (MSE) measures the average of the squares of the errors