Association Rule Mining for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm

  • Jesús SilvaEmail author
  • Mercedes Gaitan Angulo
  • Danelys Cabrera
  • Sadhana J. Kamatkar
  • Hugo Martínez Caraballo
  • Jairo Martinez Ventura
  • John Anderson Virviescas Peña
  • Juan de la Hoz – Hernandez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1046)


Customer’s segmentation is used as a marketing differentiation tool which allows organizations to understand their customers and build differentiated strategies. This research focuses on a database from the SMEs sector in Colombia, the CRISP-DM methodology was applied for the Data Mining process. The analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and the following grouping algorithms were applied on this model: k-means, k-medoids, and Self-Organizing Maps (SOM). For validating the result of the grouping algorithms and selecting the one that provides the best quality groups, the cascade evaluation technique has been used applying a classification algorithm. Finally, the Apriori algorithm was used to find associations between products for each group of customers, so determining association according to loyalty.


Data mining Apriori algorithm Dates product Association rules Hidden patterns extraction Consumer’s loyalty 


  1. 1.
    Amelec, V.: Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Adv. Sci. Lett. 21(5), 1406–1408 (2015)CrossRefGoogle Scholar
  2. 2.
    Varela, I.N., Cabrera, H.R., Lopez, C.G., Viloria, A., Gaitán, A.M., Henry, M.A.: Methodology for the reduction and integration of data in the performance measurement of industries cement plants. In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data, DMBD 2018. LNCS, vol. 10943, pp. 33–42. Springer, Cham (2018). Scholar
  3. 3.
    Lis-Gutiérrez, J.P., Lis-Gutiérrez, M., Gaitán-Angulo, M., Balaguera, M.I., Viloria, A., Santander-Abril, J.E.: Use of the industrial property system for new creations in Colombia: a departmental analysis (2000–2016). In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data, DMBD 2018. LNCS, vol. 10943, pp. 786–796. Springer, Cham (2018). Scholar
  4. 4.
    Anuradha, K., Kumar, K.A.: An e-commerce application for presuming missing items. Int. J. Comput. Trends Technol. 4, 2636–2640 (2013)Google Scholar
  5. 5.
    Larose, D.T., Larose, C.D.: Discovering Knowledge in Data (2014). Scholar
  6. 6.
    Pickrahn, I., et al.: Contamination incidents in the pre-analytical phase of forensic DNA analysis in Austria—Statistics of 17 years. Forensic Sci. Int. Genet. 31, 12–18 (2017). Scholar
  7. 7.
    de Barrios-Hernández, K.C., Contreras-Salinas, J.A., Olivero-Vega, E.: La Gestión por Procesos en las Pymes de Barranquilla: Factor Diferenciador de la Competitividad Organizacional. Información tecnológica 30(2), 103–114 (2019)CrossRefGoogle Scholar
  8. 8.
    Prajapati, D.J., Garg, S., Chauhan, N.C.: Interesting association rule mining with consistent and inconsistent rule detection from big sales data in distributed environment. Future Comput. Inform. J. 2, 19–30 (2017). Scholar
  9. 9.
    Abdullah, M., Al-Hagery, H.: Classifiers’ accuracy based on breast cancer medical data and data mining techniques. Int. J. Adv. Biotechnol. Res. 7, 976–2612 (2016)Google Scholar
  10. 10.
    Khanali, H.: A survey on improved algorithms for mining association rules. Int. J. Comput. Appl. 165, 8887 (2017)Google Scholar
  11. 11.
    Ban, T., Eto, M., Guo, S., Inoue, D., Nakao, K., Huang, R.: A study on association rule mining of darknet big data. In: 2015 International Joint Conference on Neural Networks, pp. 1–7 (2015).
  12. 12.
    Vo, B., Le, B.: Fast algorithm for mining generalized association rules. Int. J. Database Theory Appl. 2, 1–12 (2009)Google Scholar
  13. 13.
    Al-Hagery, M.A.: Knowledge discovery in the data sets of hepatitis disease for diagnosis and prediction to support and serve community. Int. J. Comput. Electron. Res. 4, 118–125 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jesús Silva
    • 1
    Email author
  • Mercedes Gaitan Angulo
    • 2
  • Danelys Cabrera
    • 3
  • Sadhana J. Kamatkar
    • 4
  • Hugo Martínez Caraballo
    • 5
  • Jairo Martinez Ventura
    • 6
  • John Anderson Virviescas Peña
    • 7
  • Juan de la Hoz – Hernandez
    • 6
  1. 1.Universidad Peruana de Ciencias AplicadasLimaPeru
  2. 2.Corporación Universitaria Empresarial de Salamanca (CUES)BarranquillaColombia
  3. 3.Universidad de la CostaBarranquillaColombia
  4. 4.University of MumbaiMumbaiIndia
  5. 5.Universidad Simón BolívarBarranquillaColombia
  6. 6.Corporación Universitaria LatinoamericanaBarranquillaColombia
  7. 7.Corporación Universitaria Minuto de Dios - UNIMINUTOBelloColombia

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