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The Association Rule Algorithm with Missing Data in Data Mining

  • Bobby D. Gerardo
  • Jaewan Lee
  • Jungsik Lee
  • Mingi Park
  • Malrey Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3043)

Abstract

This paper discusses the use of an association rule algorithm in data mining and the processes of handling missing data in a distributed database environment. The investigation generated improved association rules using the model described here. The evaluations showed that more association patterns were generated in which the algorithm for missing data was used; this suggested more rules generated than by simply ignoring them. This implies that the model offer more precise and important association rules that is more valuable when applied for business decision making. With the discovery of accurate association rules or business patterns, approach for better market plans can be prepared and implemented to improve marketing schemes. One best-related application of handling missing data is for detecting fraud or devious database entries.

Keywords

Data Mining Association Rule Frequent Itemsets Association Rule Mining Data Cube 
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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References

  1. 1.
    Agrawal, Srikant.: Fast Algorithms for Mining Association Rules. In: Proceeding of International Conference on Very Large Databases VLDB, pp. 487–499 (1994)Google Scholar
  2. 2.
    Coenen, F.: The Apriori Algorithm. (2001), http://www.csc.liv.ac.uk/~fans/ KDD/ aprioriTdemo.html#algorithm
  3. 3.
    Edelstein, Herb. Data Mining: Can you dig it? articles/2003/vol3_no2/enterpriseviews, http://www.teradatamagazine.com/
  4. 4.
    Han, J., Kamber, M.: Data mining concepts and techniques. Morgan Kaufmann, USA (2001)Google Scholar
  5. 5.
    Hellerstein, J.L., Ma, S., Perng, C.S.: Discovering actionable patterns in event data. IBM Systems Journal 41(3) (2002)Google Scholar
  6. 6.
    Multi-Dimensional Constrained Gradient Mining /JoyceManWingLamMSc.pdf (2001), ftp://fas.sfu.ca/pub/cs/theses/
  7. 7.
    Nayak, R., J., Cook, D.J.: Approximate Association Rule Mining. In: Proceedings of the Florida Artificial Intelligence Research Symposium (2001)Google Scholar
  8. 8.
    Nestorov, Svetlozar, Jukic: Nenad. Ad-Hoc Association-Rule Mining within the Data Warehouse. In: Proceedings of 36th Annual Hawaii International Conference on System Sciences, January 2003, p. 232a (2003)Google Scholar
  9. 9.
    Pairwise Deletion of Missing Data vs. Mean Substitution. http:// /textbook/glosp.html, http://www.statsoftinc.com

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Bobby D. Gerardo
    • 1
  • Jaewan Lee
    • 1
  • Jungsik Lee
    • 1
  • Mingi Park
    • 1
  • Malrey Lee
    • 2
  1. 1.School of Electronic and Information EngineeringKunsan National UniversityChonbukSouth Korea
  2. 2.School of Electronics & Information EngineeringChonbuk National UniversityChonbukSouth Korea

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