A new hybrid of Fuzzy C-Means Method and Fuzzy Linear Regression Model in Predicting Manufacturing Income

  • Authors

    • Nurfarawahida Ramly
    • Mohd Saifullah Rusiman
    • Norziha Che Him
    • Maria Elena Nor
    • Supar man
    • NurAin Zafirah Ahmad Basri
    • Nazeera Mohamad
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.30.22371
  • , Fuzzy linear regression (FLR), fuzzy c-means (FCM), mean square error (MSE)

  • 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.

  • References

    1. [1] Kaufman L & Rousseeuw PJ, Finding Groups in Data: An Introduction to Cluster Analysis, Canada: A Wiley-Interscience Publication (1989).

      [2] Everitt BS, Landau S, Leese M & Stahl D, Cluster Analysis. 5th ed., King’s College London: A John Wiley and Sons Ltd. Publication (2011).

      [3] Aggarwal CC & Reddy CK, Data Clustering: Algorithms and Applications, London: CRC Press Taylor & Francis Group (2014).

      [4] Chaudhuri A & De K, Achieving Greater Explanation Power and Forecasting Accuracy with Non-uniform spread Fuzzy Linear regression, School of Science, Netaji Subhas Open University, India (2013).

      [5] Tanaka H, Uejima S & Asai K, “Linear Regression analysis with fuzzy modelâ€, IEEE Transactions on Systems Man and Cybernetics, 12, (1982) 903-907.

      [6] Zolfaghari ZS, Mohebbi M & Najariyan M, “Application of fuzzy linear regression method for sensory evaluation of fried donut. Ferdowsi University of Mashhad, Iranâ€, Applied Soft Computing 22, (2014) 417-423.

      [7] Mendenhall W, Beaver RJ & Beaver BM, Introduction to Probability and Statistics (14th ed.), Pacific Grove, Califonia (2013).

      [8] Garcia C, Gomez RS, Perez JG & Martin, MDML, “On the selection of the ridge and raise factorsâ€, Indian Journal of Science and Technology, 10(13) (2017).

      [9] Dunn J, “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clustersâ€, Journal of Cybernetics, 3, (1973) pp. 32-57.

      [10] Bezdek JC, “Pattern Recognition with Fuzzy Objective Function Algorithmsâ€, Plenum Press, New York, (1981) doi: 10. 1007/978-1-4757-0450-1.

      [11] Maulik U & Bandyopadhyay S, “Performance evaluation of some clustering algorithms and validity indicesâ€, IEEE PAMI, 24, (2002) pp. 1650-1654.

      [12] Tan PN, Steinbach M & Kumar V, Introduction to Data Mining. USA: Addison-Wesley Longman, Incorporated (2005).

      [13] Xie XL & Beni GA, “Validity Measure for Fuzzy Clusteringâ€, IEEE Transactionson Pattern Analysis and Machine Intelligence, 13(4), (1991) pp. 841-846.

      [14] Ni Y, Fuzzy Correlation and Regression Analysis. University of Oklahoma Graduate College, UMI number : 3163014 (2005).

      [15] Ghosh S & Dubey SK, “Comparative Analysis of K-mean and Fuzzy C-means Algorithmsâ€, (IJACSA) International Journal of Advanced Computer Science and Applications, 4(4) (2013).

      [16] Xiao L, Shao W, Liang TL & Wang, CA. “Combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecastingâ€, Application Energy, 167:135e53 (2016).

      [17] Samat NA, Salleh MNM, “A study of data imputation using fuzzy c-means with particle swarm optimizationâ€, Advances in Intelligent Systems and Computing, 549 AISC (2017), 91-100

      [18] Hussain K, Mohd Salleh MN, “Analysis of techniques for ANFIS rule-base minimization and accuracy maximizationâ€, ARPN Journal of Engineering and Applied Sciences, 10 (20), (2015), 9739-9746.

      [19] Salleh MNM, “A fuzzy modelling of decision support system for crop selectionâ€, ISIEA 2012 - 2012 IEEE Symposium on Industrial Electronics and Applications, 6496622 (2012), 17-22.

      [20] Khalid K, Mohamed I and Abdullah NA, An Additive Outlier Detection Procedure in Random Coefficient Autoregressive Models AIP Conference Proceedings 1682, (2015), 050017.

      [21] Mohamed I, Khalid K And Yahya MS, Combined Estimating Function for Random Coefficient Models with Correlated Errors Communications In Statistics—Theory And Methods 45(4), (2016), 967-975.

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  • How to Cite

    Ramly, N., Rusiman, M. S., Him, N. C., Nor, M. E., man, S., Basri, N. Z. A., & Mohamad, N. (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), 473-478. https://doi.org/10.14419/ijet.v7i4.30.22371

    Received date: 2018-11-29

    Accepted date: 2018-11-29

    Published date: 2018-11-30