Incremental Association Rule Mining Using Materialized Data Mining Views

  • Mikołaj Morzy
  • Tadeusz Morzy
  • Zbyszko Królikowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3261)


Data mining is an interactive and iterative process. Users issue series of similar queries until they receive satisfying results, yet currently available data mining systems do not support iterative processing of data mining queries and do not allow to re-use the results of previous queries. Consequently, mining algorithms suffer from long processing times, which are unacceptable from the point of view of interactive data mining. On the other hand, the results of consecutive data mining queries are usually very similar. This observation leads to the idea of reusing materialized results of previous data mining queries. We present the notion of a materialized data mining view and we propose two novel algorithms which aim at efficient discovery of association rules in the presence of materialized results of previous data mining queries.


Data Mining Association Rule Frequent Itemsets Association Rule Mining Support Count 
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.) Proc. Of the 1993 ACM SIGMOD, Washington, D.C, May 26-28, pp. 207–216. ACM Press, New York (1993)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. of VLDB 1994, Santiago de Chile, Chile, September 12-15, pp. 487–499. Morgan Kaufmann, San Francisco (1994)Google Scholar
  3. 3.
    Cheung, D.W.-L., Han, J., Ng, V., Wong, C.Y.: Maintenance of discovered association rules in large databases: An incremental updating technique. In: Su, S.Y.W. (ed.) Proc. of ICDE 1996, New Orleans, Louisiana, February 26 - March 1, pp. 106–114. IEEE Computer Society, Los Alamitos (1996)Google Scholar
  4. 4.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Cambridge (1996)Google Scholar
  5. 5.
    Gupta, A., Mummick, I.S.: Maintenance of materialized views: Problems, techniques, and applications. IEEE Data Engineering Bulletin, Special Issue on Materialized View and Data Warehousing 2(18) (June 1995)Google Scholar
  6. 6.
    Gupta, A., Mummick, I.S.: Materialized Views: Techniques, Implementations, and Applications. The MIT Press, Cambridge (1999)Google Scholar
  7. 7.
    Imielinski, T., Virmani, A.: Msql: A query language for database mining. Data Mining and Knowledge Discovery 3(4), 373–408 (1999)CrossRefGoogle Scholar
  8. 8.
    Botta, M., Boulicaut, J.-F., Masson, C., Meo, R.: A Comparison between Query Languages for the Extraction of Association Rules. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 1–10. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Meo, R., Psaila, G., Ceri, S.: A new sql-like operator for mining association rules. In: Vijayaraman, T.M., Buchmann, A.P., Mohan, C., Sarda, N.L. (eds.) Proc. of VLDB 1996, Mumbai (Bombay), India, September 3-6, pp. 122–133. Morgan Kaufmann, San Francisco (1996)Google Scholar
  10. 10.
    Morzy, T., Wojciechowski, M., Zakrzewicz, M.: Materialized data mining views. In: Zighed, D.A., Komorowski, H.J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 65–74. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  11. 11.
    Morzy, T., Zakrzewicz, M.: Sql-like language for database mining. In: Proc. of ADBIS 1997. St.Petersburg, Russia (1997)Google Scholar
  12. 12.
    Roussopoulos, N.: Materialized views and data warehouses. SIGMOD Record 27(1) (1998)Google Scholar
  13. 13.
    Thomas, S., Bodagala, S., Alsabti, K., Ranka, S.: An efficient algorithm for the incremental updation of association rules in large databases. In: Heckerman, D., Mannila, H., Pregibon, D. (eds.) Proc. of KDD 1997, Newport Beach, California, USA, August 14-17, pp. 263–266. AAAI Press, Menlo Park (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Mikołaj Morzy
    • 1
  • Tadeusz Morzy
    • 1
  • Zbyszko Królikowski
    • 1
  1. 1.Institute of Computing SciencePoznań University of TechnologyPoznańPoland

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