Iran Auto Market Price Segmentation and Car Ranking in Segments Using a Hybrid DEMATEL- Two-Step Clustering-TOPSIS Approaches and two-step Weighting based on Shannon’s entropy

Document Type : Research Paper



Today, there are more than 300 types of cars in Iran auto market, which has a significant growth in recent decade. High variety have challenges for decision makers in selecting cars. No mathematical model has been developed yet for segmenting and ranking Iran auto market, which carry out both defining automatic cluster numbers as well as automatic weighting criteria by the model.
This research develops a Hybrid DEMATEL-Two-Step Clustering-TOPSIS approach. The model first finds the beat appropriated criterion for segmentation. Then uses a two-step clustering approach for segmenting Iran auto market based on price criterion. Second, the criteria will be weighted automatically using Shannon entropy weighting method and then, TOPSIS method rank competitors in each defined price segment (lower 900 Million Rials). Also, the Spearman's rank correlation test is used to compare the model results with Iranian customer behavior (with selling volume). The price segmentation results reveal that the Iran auto market can be segmented in six different levels. Furthermore, the ranking results disclose that price is not the only effective factor in finding car utility for the buyer. A weighted combination of performance, features and price will determine optimized selection for buyers


Aghabozorgi, S., SeyedShirkhorshidi, A., Wah, T.Y., (2015). Time-series clustering- A decade review, Information Systems, 53, 16-38.
Bacudio R. Lindley , Michael Francis D. Benjamin,Ramon Christian P. EusebioSed Anderson K. HolaysanMichael Angelo B. PromentillaKrista Danielle S. YuKathleen B. Aviso, (2016). Analyzing barriers to implementing industrial symbiosis networks using DEMATEL, Sustainable Production and Consumption, 7, 57–65.
Chen, X., (2015). A new clustering algorithm based on near neighbor influence, Expert Systems with Applications, 42 (21), 7746-7758.
Chiu, T., Fang, D., Chen, J., Wang, Y., & Jeris, C. (2001). A Robust and Scalable Clustering Algorithm for Mixed Type Attributes in Large Database Environment, In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 263–268.
Damilola F. Arawomo, Augustine C.Osigwe, (2016). Nexus of fuel consumption, car features and car prices: Evidence from major institutions in Ibadan, Renewable and Sustainable Energy Reviews 59, 1220–1228.
Francisco, D., (2012). Poor mental health symptoms among Romanian employees. A Two-Step Cluster analysis, Procedia - Social and Behavioral Sciences, 33, 293-297
Golchinfar, Sh., Bakhtaei, A., (2006). Market Segmentation, Tadbir Journal, 175 (in Persian).
Grace Haaf, C., Jeremy J. Michalek, W. Ross Morrow, Yimin Liu, (2014). Sensitivity of Vehicle MarketShare Predictions to DiscreteChoice Model Specification, Journal of Mechanical Design, 136 (12), 121402.
Graeme P Maxton and John Wormald, (2004). Time for a Model Change, New York: Cambridge University Press.
Heidarzade, A., Mahdavi, I., Mahdavi-Amiri, N., (2016). Supplier Selection Using a Clustering Method Based on a New Distance for Interval Type-2 Fuzzy Sets: A Case Study, Applied Soft Computing, 38, 213-231.
Huifeng, W., Xiaoyu, Z., Xiaojing, L., Peiqiu, L. Weisheng, L. Zhongfeng, L., Yijie, W., Fengkui, P., (2006). Studies on Acute Toxicity of Model Toxins by Proton Magnetic Resonance Spectroscopy of Urine Combined with Two-step Cluster Analysis, Chinese Journal of Analytical Chemistry, 34 (1), 21-25.
Luxburg, U. V., (2007). A tutorial on spectral clustering, Statistics and Computing, 17(4), 395–416.
Matas, Anna; Raymond, josep, (2006). Hedonic prices for cars: An application to the Spanish car market; Universitat Autonoma de Barcelona
Milani, A. S., Shanian, A., & El-Lahham, C., (2008). A decision-based approach for measuring human behavioral resistance to organizational change in strategic planning, Mathematical and Computer Modeling, 48, 1765–1774.
Milani, A. S., Shanian, A., Madoliat, R., & Nemes, J. A., (2005). The effect of normalization norms in multiple attribute decision making models: A case study in gear material selection. Structural and Multidisciplinary Optimization, 29, 312–318.
Min, J., Peng, K. H., (2012). Ranking emotional intelligence training needs in tour leaders: An entropy-based TOPSIS approach, Current Issues in Tourism, 15 (6), 563-576.
Ming-Yi Shih, Jar-Wen Jheng and Lien-Fu Lai, (2010). A Two-Step Method for Clustering Mixed Categorical and Numeric Data. Tamkang Journal of Science and Engineering, 13 (1), 11-19.
Momeni, M., Najafi Moghaddam, E., (2004). Performance analysis of accepted companies in Tehran Stoch Exchange using TOPSIS, Economical Research Journal, 3 (1), 55-75 (in Persian).
Rai, P., Singh, S., (2010). A survey of clustering techniques, International Journal of Compututer Applications, 7 (12), 1–5.
Rao, R. V., Davim, J. P. (2008). Decision-Making Framework Models for Material Selection Using a Combined Multiple Attribute Decision-Making Method, Journal of Advanced Manufacturing Technology, 35, 751–760.
Roy, S., Bhattacharyya, D. K., (2005). An approach to find embedded clusters using density based techniques. Lecture Notes in Computer Science, 3816, 523–535.
Satish, S.M., Bharadhwaj, S., (2010). Information search behaviour among new car buyers: A two-step cluster analysis, IIMB Management Review 22, 5-15.
Şchiopu, D., (2010). Applying TwoStep Cluster Analysis for Identifying Bank Customers’ Profile. Seria ŞtiinŃe Economice, 62 (3), 66-75.
Shao, J., Ahmadi, Z., Kramer, S., (2014). Prototype-based learning on concept-drifting data streams. In SIGKDD, 412–421.
Singh, R. K., Benyoucef, L., (2011). A fuzzy TOPSIS based approach for e-sourcing, Engineering Applications of Artificial Intelligence, 24, 437–448.
Srdjevic, B., Medeiros, Y. D. P., & Faria, A. S. (2004). An objective multi-criteria evaluation of water management scenarios. Water Resources Management, 18, 35–54.
Triantaphyllou, E., Shu, B., Sanchez, N., Ray, T., (1998). Multi-Criteria Decision Making: An Operations Research Approach, Encyclopedia of Electrical and Electronics Engineering, 15, 175-186.
Wang, Y. J., (2008). Applying FMCDM to Evaluate Financial Performance of Domestic Airlines in Taiwan, Expert Systems with Applications, 34, 1837–1845.
Zhang, G., Shang, J., Li, W., (2012). An information granulation entropy-based model for third-party logistics providers’ evaluation. International Journal of Production Research, 50 (1), 177–190.
Zhang, H., Gu, C. L., Gu, L. W., & Zhang, Y., (2011). The evaluation of tourism destination competitiveness by TOPSIS & information entropy – a case in the Yangtze River delta of China. Tourism Management, 32, 443–451.