Catalog Segmentation by Implementing Fuzzy Clustering and Mathematical Programming Model

  • Amir Hassan Zadeh
  • Hamed Maleki
  • Kamran Kianfar
  • Mehdi Fathi
  • Mohammad Saeed Zaeri
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 73)


This work is concerned with the fuzzy clustering problem of different products in j variant catalogs, each of size i products that maximize customer satisfaction level in customer relationship management. The satisfaction degree of each customer is defined as a function of his/her needed product number that exists in catalog and also his/her priority. To determine the priority level of each customer, firstly customers are divided to three clusters with high, medium and low importance based on his/her needed products list. Then, all customers have been ranked based on their membership level in each of the above three clusters. In this paper in order to cluster customers, fuzzy c-means algorithm is applied. The proposed problem is firstly modeled as a bi-objective mathematical programming model. The objective functions of the model are to maximize the number of covered customers and overall satisfaction level results of delivering service. Then, this model is changed to a single integer linear programming model by applying fuzzy theory concepts. Finally, the efficiency of the proposed solution procedure is verified by using a numerical example.


Catalog Segmentation Customer Clustering Fuzzy C-means Algorithm 


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  1. 1.
    Amiri, A.: Customer-oriented catalog segmentation: Effective solution approaches. Decision Support Systems 42, 1860–1871 (2006)CrossRefGoogle Scholar
  2. 2.
    Berget, I., et al.: New modifications and applications of fuzzy C-means Methodology. Computational Statistics & Data Analysis 52, 2403–2418 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Bradley, P.S., Fayyad, U.M., Mangasarian, O.L.: Mathematical programming for data mining: Formulations and challenges. Journal on Computing 11, 217–238 (1999)zbMATHMathSciNetGoogle Scholar
  4. 4.
    Chiger, S.: Benchmark survey on critical issues and trends Catalog, pp. 32–37 (2003)Google Scholar
  5. 5.
    Hsu, C.-C., Chen, Y.-C.: Mining of mixed data with application to catalog marketing. Expert Systems with Applications 32, 12–23 (2007)CrossRefGoogle Scholar
  6. 6.
    Ester, M., Ge, R., Jin, W., Hu, Z.: A Microeconomic Data Mining Problem: Customer-Oriented Catalog Segmentation. In: Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining, Seattle, Washington, pp. 557–562 (2004)Google Scholar
  7. 7.
    Kleinberg, J., Papadimitriou, C., Raghavan, P.: Segmentation problems. In: Proceedings of the Thirtieth Annual ACM Symposiumon Theory of Computing, pp. 473–482 (1998)Google Scholar
  8. 8.
    Lin, C., Hong, C.: Using customer knowledge in designing electronic catalog. Expert Systems with Applications 34, 119–127 (2008)CrossRefGoogle Scholar
  9. 9.
    Ngai, E.W.T., et al.: Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications (2009)Google Scholar
  10. 10.
    Rygielski, C., Wang, J.C., Yen, D.C.: Data mining techniques for customer relationship management. Technology in Society 24, 483–502 (2002)CrossRefGoogle Scholar
  11. 11.
    Turkey, M.: A mixed-integer programming approach to the clustering problem with an application in customer segmentation. European Journal of Operational Research (2006)Google Scholar
  12. 12.
    Xiujuan, X., Liu, Y., et al.: Catalog segmentation with double constraints in business. Pattern Recognition Letters 30, 440–448 (2009)CrossRefGoogle Scholar
  13. 13.
    Zimmermann, H.J.: Fuzzy Set Theory and Its Applications, 3rd edn. Kluwer academic publishers, Dordrecht (1996)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Amir Hassan Zadeh
    • 1
  • Hamed Maleki
    • 2
  • Kamran Kianfar
    • 3
  • Mehdi Fathi
    • 1
  • Mohammad Saeed Zaeri
    • 4
  1. 1.Department of Industrial EngineeringAmir Kabir University of TechnologyTehranIran
  2. 2.Department of Industrial EngineeringAzad University, South Tehran BranchTehranIran
  3. 3.Department of Industrial EngineeringIsfahan University of TechnologyIsfahanIran
  4. 4.Iran Helicopter Support & Renewal Company (IHSRC)TehranIran

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