Building Mini-Categories in Product Networks

Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 597)


We constructed a product network based on the sales data collected and provided by a major nationwide retailer. The structure of the network is dominated by small isolated components, dense clique-based communities, and sparse stars and linear chains and pendants. We used the identified structural elements (tiles) to organize products into mini-categories—compact collections of potentially complementary and substitute items. The mini-categories extend the traditional hierarchy of retail products (group–class–subcategory) and may serve as building blocks towards exploration of consumer projects and long-term customer behavior.


retailing product network mini-category category management 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tuli, K.R., Kohli, A.K., Bharadwaj, S.G.: Rethinking Customer Solutions: from Product Bundles to Relational Processes. J. of Marketing 71(3), 1–17 (2007)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. of the ACM SIGMOD International Conference on Management of Data, Washington, D.C., pp. 207–216 (1993)Google Scholar
  3. 3.
    Forte Consultancy. Product Network Analysis—the Next Big Thing in Retail Data Mining,
  4. 4.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast Unfolding of Communities in Large Networks. J. of Statistical Mechanics: Theory and Experiment 10, 10008 (2008)CrossRefGoogle Scholar
  5. 5.
    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society. Nature 435(7043), 814–818 (2005)CrossRefGoogle Scholar
  6. 6.
    Kim, H.K., Kim, J.K., Chen, Q.Y.: A Product Network Analysis for Extending the Market Basket Analysis. Expert Systems with Applications 39, 7403–7410 (2012)CrossRefGoogle Scholar
  7. 7.
    Xie, J., Kelley, S., Szymanski, B.K.: Overlapping Community Detection in Networks: The State-of-the-Art and Comparative Study. ACM Computing Surveys 45(4), 1–37 (2013)CrossRefGoogle Scholar
  8. 8.
    Raeder, T., Chawla, N.V.: Market Basket Analysis with Networks. Social Network Analysis and Mining 1(2), 97–113 (2011)CrossRefGoogle Scholar
  9. 9.
    Coscia, M., Giannotti, F., Pedreschi, D.: A Classification for Community Discovery Methods in Complex Networks. Statistical Analysis and Data Mining 4(5), 512–546 (2011)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Pennacchioli, D., Coscia, M., Pedreschi, D.: Overlap versus Partition: Marketing Classification and Customer Profiling in Complex Networks of Products. In: 2014 IEEE 30th International Conference on Data Engineering Workshops (ICDEW), pp. 103–110. IEEE (2014)Google Scholar
  11. 11.
    Videla-Cavieres, I.F., Ríos, S.A.: Extending Market Basket Analysis with Graph Mining Techniques: a Real Case. Expert Systems with Applications 41(4), 1928–1936 (2014)CrossRefGoogle Scholar
  12. 12.
    Wharton Customer Analytics Initiative: Using Purchase History to Identify Customer “Projects.” Data Key 3.0, available through WCAI (2014)Google Scholar
  13. 13.
    Johnson, D.S.: Approximation Algorithms for Combinatorial Problems. J. Comput. Syst. Sci. 9(3), 256–278 (1974)CrossRefzbMATHGoogle Scholar
  14. 14.
    Brijs, T., et al.: Building an Association Rules Framework to Improve Product Assortment Decision. Data Mining and Knowledge Discovery 8, 7–23 (2004)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Lattin, J.M., McAlister, L.: Using a Variety-Seeking Model to Identify Substitute and Complementary Relationships among Competing Products. J. of Marketing Research 22(3), 330–339 (1985)CrossRefGoogle Scholar
  16. 16.
    Elrod, T., et al.: Inferring Market Structure from Customer Response to Competing and Complementary Products. Marketing Letters 13(3), 221–232 (2002)CrossRefGoogle Scholar
  17. 17.
    Henderson, K., et al.: RolX: Structural Role Extraction and Mining in Large Graphs. In: Proc. of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1231–1239. ACM (2012)Google Scholar
  18. 18.
    Buhrmester, M., Kwang, T., Gosling, S.D.: Amazon’s Mechanical Turk: A New Source of Inexpensive, yet High-Quality, Data? Perspectives on Psychological Science 6, 3–5 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceSuffolk UniversityBostonUSA
  2. 2.Department of MarketingSuffolk UniversityBostonUSA
  3. 3.Department of Information Systems and Operations ManagementSuffolk UniversityBostonUSA

Personalised recommendations