Skip to main content
SpringerLink
Log in

Market Basket Analysis Using Community Detection Approach: A Real Case

  • Chapter
  • First Online:
Applications of Data Management and Analysis

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Market basket analysis, as an important analysis tool, helps businesses to improve product promotion and to recommend systems development. The association rules based on the Apriori algorithm is the conventional method of market basket analysis. In this research by modeling transactional data as product network, customer network, and customer-product network through applying a well-known network analysis technique, a number of hidden patterns in the connections among customers and products were detected. The required data is taken from an online retail store in Iran. Transactional data over a period of 6 months with around 320,616 transactions in 2015 was analyzed by the community detection method. Extracted networks from the database depict the complexity of patterns in customer behavior or in the behavior of co-purchased products. Ultimately, findings of this research indicated that the applied method would be more practical and expressive to discover complex relations in comparison to other traditional methods like the association rule mining model. In addition, the findings will be capable enough to be used as the basis of new algorithms for developing the recommender systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Videla-Cavieres, I. F., & Ríos, S. A. (2014). Extending market basket analysis with graph mining techniques: A real case. Expert Systems with Applications, 41(4 Part 2), 1928–1936.

    Article  Google Scholar 

  2. Raeder, T., & Chawla, N. V. (2011). Market basket analysis with networks. Social Network Analysis and Mining, 1, 1–17.

    Article  Google Scholar 

  3. Kim, H. K., Kim, J. K., & Chen, Q. Y. (2012). A product network analysis for extending the market basket analysis. Expert Systems with Applications, 39(8), 7403–7410.

    Article  Google Scholar 

  4. Phan, D. D., & Vogel, D. R. (2010). A model of customer relationship management and business intelligence systems for catalogue and online retailers. Information Management, 47(2), 69–77.

    Article  Google Scholar 

  5. Blattberg, R., Byung-Do, K., & Scott, N. (2008). Database marketing: Analyzing and managing customers (p. 875). New York: Springer.

    Book  Google Scholar 

  6. Han, J., & Kamber, M. (2006). Data mining concepts and techniques., Second ed. Waltham, MA: Elsevier.

    Google Scholar 

  7. Klemettinen, M., & Mannila, H. (1994). Finding interesting rules from large sets of discovered association rules. In Proceedings of the Third International Conference on Information and Knowledge Management (pp. 401–407).

    Google Scholar 

  8. Lee, J. S., Jun, C. H., Lee, J., & Kim, S. (2005). Classification-based collaborative filtering using market basket data. Expert Systems with Applications, 29(3), 700–704.

    Article  Google Scholar 

  9. Zhu, X., Ye, H., & Gong, S. (2009). A personalized recommendation system combining case-based reasoning and user-based collaborative filtering, In 2009 Chinese Control and Decision Conference CCDC 2009 (pp. 4026–4028).

    Google Scholar 

  10. Adomavicius, G., & Tuzhilin, A. (2005). Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.

    Article  Google Scholar 

  11. Zhao, Z.-D., & Shang, M.-S. (2010). User-based collaborative-filtering recommendation algorithms on Hadoop. In 2010 Third International Conference on Knowledge Discovery and Data Mining (pp. 478–481).

    Google Scholar 

  12. Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12–32.

    Article  Google Scholar 

  13. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceeding VLDB ’94 Proc. 20th Int. Conf. Very Large Data Bases (Vol. 1215, pp. 487–499).

    Google Scholar 

  14. Ahlemeyer-Stubbe, A., & Coleman, S. (2014). A practical guide to data mining for business and industry. West Sussex, UK: Wiley.

    Book  Google Scholar 

  15. Zaki, M. J., Parthasarathy, S., Ogihara, M., Li, W. (1997). New algorithms for fast discovery of association rules. In KDD (Vol. 7, pp. 283–286).

    Google Scholar 

  16. Petrison, L. A., Blattberg, R. C., & Wang, P. (1997). Database marketing. Journal of Interactive Marketing, 11(4), 109–125.

    Google Scholar 

  17. Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207–216.

    Article  Google Scholar 

  18. Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.

    Article  Google Scholar 

  19. Zaki, M. J. (2000). Generating non-redundant association rules, In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 34–43).

    Google Scholar 

  20. Brijs, T., Vanhoof, K., & Wets, G. (2003). Defining interestingness for association rules. International Journal “Information Theories & Applications”, 10(4), 370–376.

    Google Scholar 

  21. Tan, P.-N., Kumar, V., & Srivastava, J. (2004). Selecting the right objective measure for association analysis. Information Systems, 29(4), 293–313.

    Article  Google Scholar 

  22. Du Mouchel, W., & Pregibon, D. (2001). Empirical Bayes screening for multi-item associations, In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD’01 (pp. 67–76).

    Google Scholar 

  23. Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: reasoning about a highly connected world (Vol. 81). Cambridge University Press.

    Google Scholar 

  24. Cuvelier, E., & Aufaure, M.-A. A. (2012). Graph mining and communities detection. In M.-A. Aufaure & E. Zimányi (Eds.), Business intelligence, vol. 96 (pp. 117–138). Berlin Heidelberg: LNBIP Springer.

    Chapter  Google Scholar 

  25. Stam, C. C. J., & Reijneveld, J. C. J. (2007). Graph theoretical analysis of complex networks in the brain. Nonlinear Biomed. Phys., 1, 3.

    Article  Google Scholar 

  26. Wang, Y., & Lin, K. J. (2008). Reputation-oriented trustworthy computing in e-commerce environments. IEEE Internet Computing, 12(4), 55–59 25.

    Article  Google Scholar 

  27. Shai, O., & Preiss, K. (1999). Graph theory representations of engineering systems and their embedded knowledge. Artificial Intelligence in Engineering, 13(3), 273–285.

    Article  Google Scholar 

  28. Huang, Z., Zeng, D. D., & Chen, H. (2007). Analyzing consumer-product graphs: Empirical findings and applications in recommender systems. Management Science, 53(7), 1146–1164.

    Article  Google Scholar 

  29. McSherry, F., & Mironov, I. (2009). Differentially private recommender systems: building privacy into the netflix prize, In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (pp. 627–636).

    Google Scholar 

  30. Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of massive data., Second ed. New York, NY: Cambridge University Press.

    Book  Google Scholar 

  31. Beel, J., Langer, S., Genzmehr, M., Gipp, B., Breitinger, C., & Nürnberger, A. (2013). Research paper recommender system evaluation: A quantitative literature survey. In RepSys (pp. 15–22).

    Google Scholar 

  32. Amatriain, X., Jaimes, A., Oliver, N., & Pujol, J. (2011). Data mining methods for recommender systems. Recommender Systems Handbook, pp. 39–71.

    Google Scholar 

  33. Girvan, M., Girvan, M., Newman, M. E. J., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826.

    Article  Google Scholar 

  34. Schafer, J., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In Adaptive Web (pp. 291–324).

    Google Scholar 

  35. Newman, M. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582.

    Article  Google Scholar 

  36. Sankar, C. P., Asokan, K., & Kumar, K. S. (2015). Exploratory social network analysis of affiliation networks of Indian listed companies. Social Networks, 43, 113–120.

    Article  Google Scholar 

  37. Newman, M., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 1–16.

    Article  Google Scholar 

  38. Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications, 10(2), 191–218.

    Article  Google Scholar 

  39. Newman, M. E. J. (2004). Detecting community structure in networks. The European Physical Journal B, 38(2), 321–330.

    Article  Google Scholar 

  40. Bhowmick, S., & Srinivasan, S. (2013). A template for parallelizing the Louvain method for modularity maximization. Dynamics On and Of Complex Networks, 2, 111–124.

    Google Scholar 

  41. Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74(3), 036104.

    Article  Google Scholar 

  42. Newman, M. E. J.. Analysis of weighted networks. Physical Review E, Vol.70, No.5 2004: 056131.

    Google Scholar 

  43. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, 6.

    Article  Google Scholar 

  44. Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74(3).

    Google Scholar 

  45. Clauset, A., Newman, M. E. J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6 Pt 2), 066111.

    Article  Google Scholar 

  46. Kawonga, M., Blaauw, D., & Fonn, S. (2015). Exploring the use of social network analysis to measure communication between disease programme and district managers at sub-national level in South Africa. Social Science & Medicine, 135, 1–14.

    Article  Google Scholar 

  47. de Nooy, W., Mrvar, A., & Batagelj, V. (2005). Exploratory social network analysis with Pajek. New York, NY: Cambridge University Press.

    Book  Google Scholar 

  48. Bhaskaran, S. R., & Gilbert, S. M. (2005). Selling and leasing strategies for durable goods with complementary products. Management Science, 51(8), 1278–1290.

    Article  Google Scholar 

  49. Celdrán, A. H., Pérez, M. G., García Clemente, F. J., & Pérez, G. M. (2016). Design of a recommender system based on users’ behavior and collaborative location and tracking. Journal of Computer Science, 12, 83–94.

    Article  Google Scholar 

  50. Kim, H. K., Kim, J. K., & Ryu, Y. U. (2009). Personalized recommendation over a customer network for ubiquitous shopping. IEEE Transactions on Services Computing, 2(2), 140–151.

    Article  Google Scholar 

  51. Gregory, S. (2009). Finding overlapping communities using disjoint community detection algorithms. Studies in Computational Intelligence, 207, 47–61.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Social Science and Economics, Alzahra University, Tehran, Iran

    Sepideh Faridizadeh, Neda Abdolvand & Saeedeh Rajaee Harandi

Authors
  1. Sepideh Faridizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neda Abdolvand
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saeedeh Rajaee Harandi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Neda Abdolvand .

Editor information

Editors and Affiliations

  1. Department Electrical & Computer Engineering, University of Calgary, Calgary, AB, Canada

    Mohammad Moshirpour

  2. Department Electrical & Computer Engineering, University of Calgary, Calgary, AB, Canada

    Behrouz H. Far

  3. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Faridizadeh, S., Abdolvand, N., Rajaee Harandi, S. (2018). Market Basket Analysis Using Community Detection Approach: A Real Case. In: Moshirpour, M., Far, B., Alhajj, R. (eds) Applications of Data Management and Analysis . Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-95810-1_13

Download citation

Publish with us

Policies and ethics

Search

Navigation