Document Type : Research Paper
Authors
Abstract
By developing information technology and emerging online markets,
Planning for these markets and analyzing their details has been became a priority for beneficiary organizations. One of most important online markets in Iran is SIM card credit services that are considered as online stores. Regarding to numerous and increasing number of online malls, grouping and classification of online malls from supplier's point of view in order to forecasting cooperation is essential. In this paper, around three thousand online stores has been studied and analyzed by using one of famous supplier data and it has been clustered according to supplier's indexes. Clustering process has been done by using SOM Neural Network in two levels by K-means algorithm since it facilitates analyzing of clusters. Although various validation indicators has been developed to determine the best number of clusters but in this paper, an optimizing model of compensatory approach to indexes is presented by using combination multi criteria decision making and aggregation of various indexes
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Chiang, M. M. & Mirkin, B. (2010). Intelligent choice of the number of clusters in kmeans clustering: an experimental study with different cluster spreads. Journal of Classification, 27(1): 3-40.
Clark, S., Sarlin, P., Sharma, A. & Sisson, S. A. (2015). Increasing dependence on foreign water resources? An assessment of trends in global virtual water flows using a self-organizing time map. Ecological Informatics, 26(2): 192-202.
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Ghazanfari, M., Alizadeh, S. & Teimourpour, B. (2008). Data Mining & Knowledge Discovery. Tehran: Iran University of Science & Technology (IUST) Press. (in persian)
Hamel, L. & Brown, C. W. (2012). Improved Interpretability of the Unified Distance Matrix with Connected Components. Proceeding of the 7th International Conference on Data Mining (DMIN'11): Las Vegas Nevada, USA.
Hossein Morshedy, A. & Memarian, H. (2012). Zoning of RQD Parameter, Based on Faults and Self-Organizing Map in Semilan Dam Site. GEOSCIENCES, 21(84), 99-112.
Hu, W. & Jing, Z. (2008). Study of Customer Segmentation for Auto Services Companies Based on RFM Model. The International Conference on Innovation Management, Decamber 10-11.
Hung, C. & Tsai, C. F. (2008). Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert Systems with Applications, 34(۱): 780-788.
Hwang, C.L., & Yoon, K. (1981). Multiple Attribute decision making. Springer.
خوشه بندی فروشگاه های آنلاین از نگاه تأمین کننده با کمک بهینه یابی... 144
Kalyani, S. & Swarup, K. S. (2011). Particle swarm optimization based K-means clustering approach for security assessment in power systems. Expert Systems with Applications, 38(9), 10839–10846.
Khadivar, A., Razmi, Z. & Hamedi, P. (2014). Customer clustering for appointing rebating strategies, case study: Kadbano Co. New Marketing Research, 3(3), 135-150.
Kim, K. J. & Ahn, H. (2008). A recommender system using GA K-means clustering in an online shopping market. Expert Systems with Applications, 34(2): 1200-1209.
Kohonen, T. (1989). Self-Organization and Associative Memory. Berlin: Springer.
Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21(1): 1-6.
Kotler, P. H. & Armstrong, G. (2013). Principles of Marketing, 14th Edition, New Jersey: Prentice Hall.
Liang, Y. H. (2010). Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Systems with Applications, 37(12): 7489-7496.
Lin, Y., Lee, P., Chang, T. & Ting, H. (2008). Multi attribute group decision making model under the condition of uncertain information. Automation in construction, 17(6), 792-797.
López, J. J., Aguado, J. A., Martín, F., Muñoz, F., Rodríguez, A. & Ruiz, J. E. (2011). Hopfield–K-Means clustering algorithm: A proposal for the segmentation of electricity customers. Electric Power Systems Research, 81(2):716-724.
Momeni, M. (2011). Data Clustering: Cluster Analysis. Tehran: Mansoor Momeni. (in persian)
Redmond, S., & Heneghan, C. (2007). A method for initialising the K-means clustering algorithm using KD-trees. Pattern Recognition Letters, 28(8), 965-973.
Rouseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20(1): 53-65.
مطالعات مدیریت صنعتی، سال چهاردهم، شماره 34 ، زمستان 59
143
Shahbaba, M. & Beheshti, S. (2014). MACE-means clustering. Signal Processing, 105:216-225.
Shim, S., Estlick, M. A., Lotz, S. L. & Warrington, P. (2001). An online prepurhcase intentions model: the role of intention to search. Journal of Retailing, 77(3): 397-416.
Thawonmas, R., Kurashige, M., Iizuka, K. & Kantardzic, M. (2006). Clustering of Online Game Users Based on Their Trails Using Self-organizing Map. Lecture Notes in Computer Science, (4161): 336-369.
Ultsch, A. & Siemon, H. P. (1990). Kohonen's Self Organizing Feature Maps for Exploratory Data Analysis. In Widrow, Bernard; Angeniol, Bernard. Proceedings of the International Neural Network Conference (INNC-90), Paris, France, July 9–13.
Vesanto, J., & Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE Transactions on Neural Networks, 11(3), 586-600.
Wang, Y., Ma, X., Lao, Y. & Wang, Y. (2014). A fuzzy-based customer clustering approach with hierarchical structure for logistics network optimization. Expert Systems with Applications, 41(2), 521-534.
Wang, Z., Bian, S., Liu, Y. & Liu, Z. (2013). The load characteristics classification and synthesis of substations in large area power grid. Electrical Power and Energy Systems, ۸۴: ۱۱-۴۸.
Yu, P. & Lee, J. H. (2013). A hybrid approach using two-level SOM and combined AHP rating and AHP/DEA-AR method for selecting optimal promising emerging technology. Expert Systems with Applications, 40(1), 300-314.
Yusof, N. M. (2006). Multilevel learning in Kohonen SOM network for classification problems. Masters thesis, Universiti Teknologi Malaysia
Chiang, M. M. & Mirkin, B. (2010). Intelligent choice of the number of clusters in kmeans clustering: an experimental study with different cluster spreads. Journal of Classification, 27(1): 3-40.
Clark, S., Sarlin, P., Sharma, A. & Sisson, S. A. (2015). Increasing dependence on foreign water resources? An assessment of trends in global virtual water flows using a self-organizing time map. Ecological Informatics, 26(2): 192-202.
Davies, D. L., & Bouldin, D. W. (1979). A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2): 224–227.
Dunn, J. C. (1974). Well separated clusters and optimal fuzzy partitions. Journal of Cybernetica, 4(1): 95- 104.
Ghazanfari, M., Alizadeh, S. & Teimourpour, B. (2008). Data Mining & Knowledge Discovery. Tehran: Iran University of Science & Technology (IUST) Press. (in persian)
Hamel, L. & Brown, C. W. (2012). Improved Interpretability of the Unified Distance Matrix with Connected Components. Proceeding of the 7th International Conference on Data Mining (DMIN'11): Las Vegas Nevada, USA.
Hossein Morshedy, A. & Memarian, H. (2012). Zoning of RQD Parameter, Based on Faults and Self-Organizing Map in Semilan Dam Site. GEOSCIENCES, 21(84), 99-112.
Hu, W. & Jing, Z. (2008). Study of Customer Segmentation for Auto Services Companies Based on RFM Model. The International Conference on Innovation Management, Decamber 10-11.
Hung, C. & Tsai, C. F. (2008). Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert Systems with Applications, 34(۱): 780-788.
Hwang, C.L., & Yoon, K. (1981). Multiple Attribute decision making. Springer.
خوشه بندی فروشگاه های آنلاین از نگاه تأمین کننده با کمک بهینه یابی... 144
Kalyani, S. & Swarup, K. S. (2011). Particle swarm optimization based K-means clustering approach for security assessment in power systems. Expert Systems with Applications, 38(9), 10839–10846.
Khadivar, A., Razmi, Z. & Hamedi, P. (2014). Customer clustering for appointing rebating strategies, case study: Kadbano Co. New Marketing Research, 3(3), 135-150.
Kim, K. J. & Ahn, H. (2008). A recommender system using GA K-means clustering in an online shopping market. Expert Systems with Applications, 34(2): 1200-1209.
Kohonen, T. (1989). Self-Organization and Associative Memory. Berlin: Springer.
Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21(1): 1-6.
Kotler, P. H. & Armstrong, G. (2013). Principles of Marketing, 14th Edition, New Jersey: Prentice Hall.
Liang, Y. H. (2010). Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Systems with Applications, 37(12): 7489-7496.
Lin, Y., Lee, P., Chang, T. & Ting, H. (2008). Multi attribute group decision making model under the condition of uncertain information. Automation in construction, 17(6), 792-797.
López, J. J., Aguado, J. A., Martín, F., Muñoz, F., Rodríguez, A. & Ruiz, J. E. (2011). Hopfield–K-Means clustering algorithm: A proposal for the segmentation of electricity customers. Electric Power Systems Research, 81(2):716-724.
Momeni, M. (2011). Data Clustering: Cluster Analysis. Tehran: Mansoor Momeni. (in persian)
Redmond, S., & Heneghan, C. (2007). A method for initialising the K-means clustering algorithm using KD-trees. Pattern Recognition Letters, 28(8), 965-973.
Rouseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20(1): 53-65.
مطالعات مدیریت صنعتی، سال چهاردهم، شماره 34 ، زمستان 59
143
Shahbaba, M. & Beheshti, S. (2014). MACE-means clustering. Signal Processing, 105:216-225.
Shim, S., Estlick, M. A., Lotz, S. L. & Warrington, P. (2001). An online prepurhcase intentions model: the role of intention to search. Journal of Retailing, 77(3): 397-416.
Thawonmas, R., Kurashige, M., Iizuka, K. & Kantardzic, M. (2006). Clustering of Online Game Users Based on Their Trails Using Self-organizing Map. Lecture Notes in Computer Science, (4161): 336-369.
Ultsch, A. & Siemon, H. P. (1990). Kohonen's Self Organizing Feature Maps for Exploratory Data Analysis. In Widrow, Bernard; Angeniol, Bernard. Proceedings of the International Neural Network Conference (INNC-90), Paris, France, July 9–13.
Vesanto, J., & Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE Transactions on Neural Networks, 11(3), 586-600.
Wang, Y., Ma, X., Lao, Y. & Wang, Y. (2014). A fuzzy-based customer clustering approach with hierarchical structure for logistics network optimization. Expert Systems with Applications, 41(2), 521-534.
Wang, Z., Bian, S., Liu, Y. & Liu, Z. (2013). The load characteristics classification and synthesis of substations in large area power grid. Electrical Power and Energy Systems, ۸۴: ۱۱-۴۸.
Yu, P. & Lee, J. H. (2013). A hybrid approach using two-level SOM and combined AHP rating and AHP/DEA-AR method for selecting optimal promising emerging technology. Expert Systems with Applications, 40(1), 300-314.
Yusof, N. M. (2006). Multilevel learning in Kohonen SOM network for classification problems. Masters thesis, Universiti Teknologi Malaysia
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