Mining Multidimensional Frequent Patterns from Relational Database

  • Yue-Shi Lee
  • Show-Jane Yen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7802)


Mining frequent patterns focus on discover the set of items which were frequently purchased together, which is an important data mining task and has broad applications. However, traditional frequent pattern mining does not consider the characteristics of the customers, such that the frequent patterns for some specific customer groups cannot be found. Multidimensional frequent pattern mining can find the frequent patterns according to the characteristics of the customer. Therefore, we can promote or recommend the products to a customer according to the characteristics of the customer. However, the characteristics of the customers may be the continuous data, but frequent pattern mining only can process categorical data. This paper proposes an efficient approach for mining multidimensional frequent pattern, which combines the clustering algorithm to automatically discretize numerical-type attributes without experts.


Data Mining Clustering Discretization Multidimensional Frequent Pattern 


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  1. 1.
    Agrawal, R., et al.: Fast Algorithm for Mining Association Rules. In: Proceedings of International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  2. 2.
    Chiang, J., Wu, C.C.: Mining multi-dimensional association rules in multiple database segmentation. In: Proceedings of International Conference on Information Management (2005)Google Scholar
  3. 3.
    El-Hajj, M., Zaiane, O.R.: Non Recursive Generation of Frequent K-itemsets from Frequent Pattern Tree Representation. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2003. LNCS, vol. 2737, pp. 371–380. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Frawley, W., Piatetsky-Shapiro, G., Matheus, C.: Knowledge Discovery in Databases: An Overview. AI Magazine, 213–228 (1992)Google Scholar
  5. 5.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns without Candidate Generation: A Frequent- Pattern Tree Approach. Data Mining and Knowledge Discovery 8(1), 53–87 (2004)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Han, J., Lakshmanan, L.V.S., Ng, R.T.: Constraint-Based, Multidimensional Data Mining. IEEE Computer 32(8), 46–50 (1999)CrossRefGoogle Scholar
  7. 7.
    Lee, Y.S., Yen, S.J., Lin, S.S., Liu, Y.C.: Integrating Multidimensional Association Rule Mining into Classification. In: Proceedings of International Conference on Informatics, Cybernetics, and Systems, pp. 831–836 (2003)Google Scholar
  8. 8.
    Park, J.S., Chen, M.S., Yu, P.S.: An Effective Hash-Based Algorithm for Mining Association Rules. Proceedings of ACM SIGMOD 24(2), 175–186 (1995)CrossRefGoogle Scholar
  9. 9.
    Tasi, S.M., Chen, C.-M.: Mining interesting association rules from customer databases and transaction databases. Information Systems 29(8), 685–696 (2004)CrossRefGoogle Scholar
  10. 10.
    Xu, W., Wang, R.: A Novel Algorithm of Mining Multidimensional Association Rules. In: Proceedings of International Conference on Intelligent Computing, pp. 771–777 (2006)Google Scholar
  11. 11.
    Yen, S.J., Chen, A.L.P.: An Efficient Approach to Discovering Knowledge from Large Databases. In: Proceedings of the International Conference on Parallel and Distributed Information Systems, pp. 8–18 (1996)Google Scholar
  12. 12.
    Yen, S.J., Wang, C.K., Ouyang, L.Y.: A Search Space Algorithm for Mining Frequent Patterns. Journal of Information Science and Engineering (JISE): Special Issue on Technologies and Applications of Artificial Intelligence 28(1), 177–191 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yue-Shi Lee
    • 1
  • Show-Jane Yen
    • 1
  1. 1.Department of Computer Science & Information EngineeringMing Chuan UniversityTaoyuan CountyTaiwan

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