توسعه یک روش هوشمند خوشه‌بندی چندمعیاره مبتنی بر پرامتی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار مدیریت صنعتی، واحد الکترونیکی، دانشگاه آزاد اسلامی، تهران، ایران.

2 استادیار مدیریت صنعتی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران.

3 کارشناسی ارشد مدیریت صنعتی، واحد الکترونیکی، دانشگاه آزاد اسلامی، تهران، ایران.

چکیده

در سال‌­های اخیر مسئله جدیدی با عنوان «خوشه‌­بندی چند­معیاره» ظهور کرده که هدف آن، دسته‌بندی گزینه­‌ها در گروه‌­های همگنی به نام خوشه با توجه به معیارهای ارزیابی متفاوت است. در ادامه پژوهش­‌های انجام­‌گرفته در مبانی نظری، پژوهش حاضر با ترکیب الگوریتم K- میانگین و تکنیک پرامتی، به­‌دنبال توسعه یک روش جدید خوشه­‌بندی چندمعیاره است. پارامترهای مسئله، پروفایل­‌های جدا­کننده خوشه‌­ها هستند که برای بهینه­‌سازی آن­ها از الگوریتم ژنتیک استفاده شده است. برای تنظیم پارامترهای ژنتیک نیز از روش تاگوچی استفاده می­‌شود. در این مدل­‌سازی، متغیرها در هر مرحله از به‌روزرسانی جواب­‌ها، با توجه به فاصله امتیاز جریان خالص خود از پروفایل­‌ها به نزدیک‌ترین خوشه تخصیص می­‌یابند. عملگر جهش نیز صرفاً زمانی اعمال می­‌شود که میزان شباهت کروموزوم­‌ها در هر جمعیت به حد خاصی برسد که این هوشمند­سازی موجب کاهش زمان محاسباتی شده است. درنهایت با اجرای روش پیشنهادی بر روی چند نمونه مسائل تصادفی مالی، عملکرد آن با سایر الگوریتم­‌های شناخته­‌شده خوشه­‌بندی مقایسه شده است. نتایج نشان می­‌دهد که روش پیشنهادی ضمن تعیین تعداد بهینه خوشه‌­ها، در مقایسه با سایر الگوریتم‌­ها، جواب­‌های دقیق­‌تری ارائه می‌­دهد.

کلیدواژه‌ها


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).

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.