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Fig. 14 | Financial Innovation

Fig. 14

From: A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks

Fig. 14

K-means 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 k-means is shown in Fig. 14. The error mean is = − 1.7356e-0.5 and std. = 0.00020128. The Mean squared error (MSE) measures the average of the squares of the errors

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