Category-Driven Association Rule Mining

  • Zina M. Ibrahim
  • Honghan Wu
  • Robbie Mallah
  • Richard J. B. Dobson
Conference paper


The quality of rules generated by ontology-driven association rule mining algorithms is constrained by the algorithm’s effectiveness in exploiting the usually large ontology in the mining process. We present a framework built around superimposing a hierarchical graph structure on a given ontology to divide the rule mining problem into disjoint subproblems whose solutions can be iteratively joined to find global associations. We present a new metric for evaluating the interestingness of generated rules based on where their constructs fall within the ontology. Our metric is anti-monotonic on subsets, making it usable in an Apriori-like algorithm which we present here. The algorithm categorises the ontology into disjoint subsets utilising the hierarchical graph structure and uses the metric to find associations in each, joining the results using the guidance of anti-monotonicity. The algorithm optionally embeds built-in definitions of user-specified filters to reflect user preferences. We evaluate the resulting model using a large collection of patient health records.


Association rule mining Ontologies Big data 



The authors would like to acknowledge the National Institute for Health Research (NIHR) Biomedical Research Centre and Dementia Unit at South London and Maudsley NHS Foundation Trust and Kings College London.


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOID. 207-216. ACM Press (1993)Google Scholar
  2. 2.
    Baclawski, K., Schneider, T.: The open ontology repository initiative: requirements and research challenges. In: Workshop on Collaborative Management of Structured Knowledge at the ISWC (2009)Google Scholar
  3. 3.
    Cherfi, H., Napoli, A., Toussaint, Y.: Towards a text mining methodology using association rule extraction. Int. J. Soft Comput. 10, 431–441 (2006)CrossRefGoogle Scholar
  4. 4.
    Cornet, R., de Keizer, N.: Forty years of SNOMED: a literature review. BMC Med. Inf. Decis. Making 8(1), S2 (2008)Google Scholar
  5. 5.
    Fernandez, J., Gonzalez, J.: Hierarchical graph search for mobile robot path planning. In: IEEE ICRA, pp. 656–661 (1998)Google Scholar
  6. 6.
    Garbacz, P., Trypuz, R.: A metaontology for applied ontology. Appl. Ontol. 8, 1–30 (2013)Google Scholar
  7. 7.
    Herre, H., Loebe, F.: A meta-ontological architecture for foundational ontologies. On the Move to Meaningful Internet Systems, pp. 1398–1415 (2005)Google Scholar
  8. 8.
    Janetzko, D., Cherfi, H., Kennke, R., Napoli, A., Toussaint, Y.: Knowledge-based selection of association rules for text mining. In: ECAI, pp. 485–489 (2004)Google Scholar
  9. 9.
    Lieber, J., Napoli, A., Szathmary, L.: First elements on knowledge discovery guided by domain knowledge. Concept Lattices Appl. 4923, 22–41 (2008)CrossRefGoogle Scholar
  10. 10.
    Marinica, C., Guillet, F.: Knowledge based interactive postmining of association rules using ontologies. IEEE Trans. Knowl. Data Eng. 22(6), 784–797 (2010)CrossRefGoogle Scholar
  11. 11.
    Palma, P., Hartmann, J., Haase, P.: Ontology metadata vocabulary for the semantic web. Technical Report, Universidad Politcnica de Madrid, University of Karlsruhe (2008)Google Scholar
  12. 12.
    Ramesh, C., Ramana, K., Rao, K., Sastry, C.: Interactive post-mining association rules using cost complexity pruning and ontologies KDD. Int. J. Comput. Appl. 68(20), 16–21 (2013)Google Scholar
  13. 13.
    Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: VLDB, pp. 432–444 (1995)Google Scholar
  14. 14.
    Singh, L., Chen, B., Haight, R., Scheuermann, P.: An algorithm for constrained association rule mining in semi-structured data. In: PAKDD, pp. 148–158 (1999)Google Scholar
  15. 15.
    Song, S., Kim, E., Kim, H., Kumar, H.: Query-based association rule mining supporting user perspective. Computing 93, 1–25 (2011)CrossRefzbMATHGoogle Scholar
  16. 16.
    Srikant, R., Vu, Q., Agrawal, R.: Mining association rules with item constraints. In: KDD, pp. 67–73 (1997)Google Scholar
  17. 17.
    Tan, P., Steinbach, M.: Introduction to Data Mining. Addison-Wesley (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Zina M. Ibrahim
    • 1
  • Honghan Wu
    • 1
  • Robbie Mallah
    • 2
  • Richard J. B. Dobson
    • 1
  1. 1.Department of Biostatistics and Health Informatics, King’s College LondonLondonUK
  2. 2.The South London and Maudsley NHS Foundation TrustLondonUK

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