Developing an Intelligent Multi Criteria Clustering Method Based on PROMETHEE

Document Type : Original Article

Authors

1 Assistant Professor in Industrial Management, Electronic Branch, Islamic Azad University, Tehran, Iran.

2 Assistant Professor in Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran.

3 M.A. in Management of Information Technology, Electronic Branch, Islamic Azad University, Tehran, Iran.

Abstract

In recent years, a new issue called "multi-criteria clustering" has emerged that aims at grouping alternatives into homogeneous classes called clusters according to different evaluation criteria. Following the related studies in literature, by combining K-means algorithm and PROMETHEE technique, this paper aims to present a new multi-criteria clustering method. The parameters of the problem are the cluster separator profiles which genetic algorithm (GA) is used to optimize them. In the modeling process in each stage of updating responses, alternatives allocate to the nearest cluster according to the distance of their pure flow of privileges from the profiles. The mutation operator is only applied when the chromosomes’ similarity level in each population reaches to a certain level which this intelligence reduces the computation time. Finally, by simulating the proposed algorithm and some well-known clustering algorithms based on the several financial databases the efficiency of the algorithm compared to other algorithms. The results show the algorithm, in addition to determine the optimal number of clusters in comparison to other algorithms, also provides better results.

Keywords

References

1. Ahmadzadehgoli, N., Behzadi, M. H., & Mohammadpour, A. (2018). Clustering with Intelligent Linex K-means. New Researches in Mathematics, 14, 5-14 (In Persian).
2. Aliheidari Bioki, T., & Khademi Zare, H. (2015). Improvement of DEA approach for clustering credit rating of customer in banks. Modeling in Engineering, 41, 59-74 (In Persian).
3. Alizadeh A, & Pooya A. (2017). Evaluating and clustering the Iranian banks and financial institutions based on website traffic indicators. Organizational Resource Management Researches, 7(1), 189-206 (In Persian).
4. Almeida, A. T., & Vetschera, R. (2012) A PROMETHEE-based approach to portfolio selection problems. Computers & Operations Research, 39(5), 1010-1020.
5. Asgharizadeh, E. A., Bitaraf, A., & Ajeli, M. (2011). Developing a hybrid model using fuzzy PROMETHEE and multi-objective linear planning for outsourcing of warranty services, Industrial Management Perspective, 2(2), 43-60 (In Persian).
6. Azizi, S., & Balaghi Inanlou, M. H., (2016). Segmentation of Mobile Banking Users Based on Expectations: A Clustering Technique. Production and Operations Management, 7(2), 217-234 (In Persian).
7. Baroudi, R., Safia, N.B. (2010). Towards multicriteria analysis: a new clustering
approach, in: Proceedings of the 2010 International Conference on Machine
and Web Intelligence, pp. 126–131.
8. Brans, J. P., & Mareschal, B. (2005). PROMETHEE methods. In Multiple criteria decision analysis: state of the art surveys. (163-186). New York, New York: Springer.
9. Brans, J. P., & Vincke, P. (1985). PROMETHEE method for multiple criteria decision making. Management Science, 31, 647-656.
10. Capó, M., Pérez, A., & Lozano, J. A. (2017). An efficient approximation to the K-means clustering for massive data. Knowledge-Based Systems, 117, 56-69.
11. Cavalcante, C. A. V., Ferreira, R. J. P., & de Almeida, A. T. (2010). A preventive maintenance decision model based on multi-criteria method PROMETHEE II integrated with Bayesian approach. IMA Journal of Management Mathematics, 21(4), 333-348.
12. Costa, C. B., De Corte, J. M. & Vansnick, J. C. (2005). On the mathematical foundations of MACBETH, in Multiple Criteria Decision Analysis: state of the art surveys, Springer, 78, 409-437.
13. De Smet, Y. (2013). P2CLUST: An extension of PROMETHEE II for multi-criteria ordered clustering. (2013). IEEE International Conference on Industrial Engineering and Engineering Management. Bangkok, Thailand.
14. De Smet, Y., & Guzmán, L. M. (2004). Toward multi-criteria clustering: An extension of the k-means algorithm. European Journal of Operational Research, 158(2), 390-398.
15. De Smet, Y., Nemery, P., & Selvaraj, R. (2012). An exact algorithm for the multi-criteria ordered clustering problem. Omega, 40(6), 861-869.
16. Dyer, J. S. (2005). Multi-Attribute Utility Theory (MAUT). In Multiple Criteria Decision Analysis: State of the Art.(265-292). New York, New York: Springer.
17. Faezi-Rad, M. A., & Pooya, A., (2016). Clustering of Online Stores from Supplier’s point of view: Using Clusters Number Optimization in Two-Level SOM. Industrial Management Studies, 34, 905-943 (In Persian).
18. Fernandez, E., Navarro, J., & Bernal, S. (2010). Handling multicriteria preferences in cluster analysis, European Journal of Operational Research, 202(3), 819–827.
19. Figueira, J., Mousseau, V., & Roy, B. (2005). ELECTRE Methods. Multiple Criteria Decision Analysis: State of the Art Surveys. In Multiple Criteria Decision Analysis: State of the Art.(133-153). New York, New York: Springer.
20. Ghorbanpour A, Tallai G, & Panahi M. (2015). Clustering Customers of Refah Bank Branches Using Combination of Genetic Algorithm and C- Means in Fuzzy Environment. Organizational Resources Management Researches, 5(3), 153-168 (In Persian).
21. Hamedi, P., Khadivar, A., & Razmi, Z. (2013). Customer clustering for appointing rebating strategies, case study: Kadbano Co. New Marketing Research, 3(3), 135-150 (In Persian).
22. Han, J., Kamber, M., & Pei, J., (2011). Data Mining: Concepts and Techniques. 3rd edition, Morgan Kaufmann.
23. Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. U Michigan Press.
24. Imani, A., & Abbasi, M. (2017). Customers Clustering Based on RFM Model by Using Fuzzy C-means Algorithm (Case Study: Zahedan City Refah Chain Store). Public Management Researches, 37, 251-276 (In Persian).
25. Islam, M. Z., Estivill-Castro, V., & Rahman, M. A., Bossomaier, T. (2018). Combining K-Means and a genetic algorithm through a novel arrangement of genetic operators for high quality clustering, Expert Systems with Applications, 91, 402-417.
26. Jafarnejad, A., Mohseni, M., & Abdollahi, A. (2014). Developing a hybrid fuzzy PROMETHEE-AHP approach for performance evaluation of the service supply chain (Case Study: Hospitality Industry), Industrial Management Perspective, 4(2), (In Persian).
27. Keshavarz Hadadha, A., Jalili Bal, Z., & Haji Yakhcha, S. (2018). Multi Criteria Decision Making Techniques and Knapsack Approach for Clustering, Evaluating and Selecting Projects. Industrial Management Studies, 50, 229-255 (In Persian).
28. Khadivar, A., & Hamedi, P. (2015). Providing Synthetic Data Mining Model Using Association Rules and Clustering for Determining Discounting Strategy (Case Study: Pegah Distribution Co). Business Strategies, 5(3), 39-52 (In Persian).
29. Khadivar, A., & Mojibian, F. (2018). Workshops Clustering Using a Combination Approach of Data Mining and MCDM. Modern Researches in Decision Making, 3(2), 107-128 (In Persian).
30. Krink, T., Paterlini, S., & Resti, A. (2007). Using differential evolution to improve the accuracy of bank rating systems. Computational Statistics & Data Analysis, 52(1), 68-87.
31. Kumar, S., (2018). Optimal cluster analysis using hybrid K-Means and Ant Lion Optimizer. Karbala International Journal of Modern Science (In press).
32. Lai, R. K., Fan, C. Y., Huang, W. H., & Chang, P. C. (2018). Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Systems with Applications, 36 (2), 3761-3773.
33. Liao, S. H., Ho, H. H., & Lin, H. W. (2008). Mining stock category association and cluster on Taiwan stock market. Expert Systems with Applications, 35(1), 19-28.
34. Meyer, P., & Olteanu, A. L. (2013). Formalizing and solving the problem of clustering in MCDA, European Journal of Operational Research, 227(3), 494-502.
35. Montgomery, D. C. (2009). Design and Analysis of Experiments. 8th Edition, Wiley & Sons, Inc.
36. Nabiloo, M., & Daneshpour, N. (2017). A clustering algorithm for categorical data with combining measures. Soft Computing Journal, 5(1), 14-25 (In Persian).
37. Olson, D., & Zoubi, A. T. (2008). Using accounting ratios to distinguish between Islamic and conventional banks in the GCC region. The International Journal of Accounting, 43(1), 45-65.
38. Omidi, M., Razavi, H., & Mahpeykar, M. R. (2011). Selection of project team members based on the effectiveness criteria and PROMETHEE method, Industrial Management Perspective, 2(1), 113-134 (In Persian).
39. Rocha, C., Dias, L. C., & Dimas, I. (2013). Multi-criteria classification with unknown categories: A clustering-sorting approach and an application to conflict management. Journal of Multi-Criteria Decision Analysis, 20, 13-27.
40. Saaty, T. L. (2005). The analytic hierarchy and analytic network processes for the measurement of intangible criteria and for decision-making. Multiple Criteria Decision Analysis: State of the Art Surveys. In Multiple Criteria Decision Analysis: State of the Art.(345-405). New York, New York; Springer.
41. Sarrazin, R., De Smet, Y., & Rosenfeld, J. (2018). An extension of PROMETHEE to interval clustering. Omega, 80, 12-21.
42. Seifoddini, H., & Wolfe, P. M. (1986). Application of the similarity coefficient method in group technology. IIE Transactions, 18(3), 271–277.
43. Silva, V. B., Morais, D. C., & Almeida, A. T. (2010). A multi-criteria group decision model to support watershed committees in Brazil. Water Resources Management, 24(14), 4075-4091.
44. Yaghini, M., & Vard, M. (2012). Automatic Clustering of Mixed Data Using Genetic Algorithm. Industrial Engineering & Production Management, 23(2), 187-197 (In Persian).
45. Yang, F., Sun, T., & Zhang, C., (2009). An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization. Expert Systems with Applications, 36(9), 847-852 (In Persian).
46. Yu, S. S., Chu, S. W., Wang, C. M., Chan, Y. K., & Chang, T. C. (2017). Two improved k-means algorithms. Applied Soft Computing, 68, 747-755.
47. Zare Ahmadabadi, H., Rafiei Omam, M., & Naser Sadr Abadi, A. (2016). Market Clustering with Ant Colony Optimization (Comparative approach with k-means). Business Administration research, 16, 17-36 (In Persian).
48. Zhai, J., Cao, Y., Yao, Y., Ding, X., & Li, Y. (2017). Coarse and fine identification of collusive clique in financial market. Expert Systems with Applications, 69, 225-238.
49. Zhang, Y., Wang, C. D., Huang, D., Zheng, W. S., & Zhou, Y. R., (2018). TW-Co-k-means: Two-level weighted collaborative k-means for multi-view clustering. Knowledge-Based Systems, 150, 127-138.
50. Zhao, Y., Ming, Y., Liu, X., Zhu, E., Zhao, K., & Yin, J. (2018). Large-scale k-means clustering via variance reduction. Neuro-computing, 307, 184-194.

Files

Share

How to cite

Statistics