Using an Interest Ontology for Improved Support in Rule Mining

  • Xiaoming Chen
  • Xuan Zhou
  • Richard Scherl
  • James Geller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)


This paper describes the use of a concept hierarchy for improving the results of association rule mining. Given a large set of tuples with demographic information and personal interest information, association rules can be derived, that associate ages and gender with interests. However, there are two problems. Some data sets are too sparse for coming up with rules with high support. Secondly, some data sets with abstract interests do not represent the actual interests well. To overcome these problems, we are preprocessing the data tuples using an ontology of interests. Thus, interests within tuples that are very specific are replaced by more general interests retrieved from the interest ontology. This results in many more tuples at a more general level. Feeding those tuples to an association rule miner results in rules that have better support and that better represent the reality.


Data Mining Association Rule Rule Mining Association Rule Mining Concept Hierarchy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., pp. 207–216 (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pp. 487–499. Morgan Kaufmann, San Francisco (1994)Google Scholar
  3. 3.
    Fortin, S., Liu, L.: An object-oriented approach to multi-level association rule mining. In: Proceedings of the fifth international conference on Information and knowledge management, pp. 65–72. ACM Press, New York (1996)Google Scholar
  4. 4.
    Geller, J., Scherl, R., Perl, Y.: Mining the web for target marketing information. In: Proceedings of CollECTeR, Toulouse, France (2002)Google Scholar
  5. 5.
    Han, J.: Mining knowledge at multiple concept levels. In: CIKM, pp. 19–24 (1995)Google Scholar
  6. 6.
    Han, J., Fu, Y.: Discovery of multiple-level association rules from large databases. In: Proc. of 1995 Int’l Conf. on Very Large Data Bases (VLDB 1995), Zürich, Switzerland, pp. 420–431 (September 1995)Google Scholar
  7. 7.
    Han, J., Fu, Y., Wang, W., Koperski, K., Zaiane, O.: DMQL: A data mining query language for relational databases (1996)Google Scholar
  8. 8.
    Joshi, M.V., Agarwal, R.C., Kumar, V.: Mining needle in a haystack: classifying rare classes via two-phase rule induction. SIGMOD Record (ACM Special Interest Group on Management of Data) 30(2), 91–102 (2001)Google Scholar
  9. 9.
    Psaila, G., Lanzi, P.L.: Hierarchy-based mining of association rules in data warehouses. In: Proceedings of the 2000 ACM symposium on Applied computing 2000, pp. 307–312. ACM Press, New York (2000)CrossRefGoogle Scholar
  10. 10.
    Páircéir, R., McClean, S., Scotney, B.: Discovery of multi-level rules and exceptions from a distributed database. In: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 523–532. ACM Press, New York (2000)CrossRefGoogle Scholar
  11. 11.
    Scherl, R., Geller, J.: Global communities, marketing and web mining. Journal of Doing Business Across Borders  1(2), 141–150 (2002)
  12. 12.
    Srikant, R., Agrawal, R.: Mining generalized association rules. In: Proc. of 1995 Int’l Conf. on Very Large Data Bases (VLDB 1995), Zürich, Switzerland, pp. 407–419 (September 1995)Google Scholar
  13. 13.
    Witten, I.H., Frank, E.: Data Mining. Morgan Kaufmann Publishers, San Francisco (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Xiaoming Chen
    • 1
  • Xuan Zhou
    • 1
  • Richard Scherl
    • 2
  • James Geller
    • 1
  1. 1.CS Dept.New Jersey Institute of TechnologyNewarkUSA
  2. 2.West Long BranchMonmouth University

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