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The Association Rule Algorithm with Missing Data in Data Mining

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Computational Science and Its Applications – ICCSA 2004 (ICCSA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3043))

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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.

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References

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Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Kunsan National University, 68 Miryong-dong, Kunsan, Chonbuk, 573-701, South Korea

    Bobby D. Gerardo, Jaewan Lee, Jungsik Lee & Mingi Park

  2. School of Electronics & Information Engineering, Chonbuk National University, 664-14, Deokjin-dong, Jeonju, Chonbuk, 561-756, South Korea

    Malrey Lee

Authors
  1. Bobby D. Gerardo
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  2. Jaewan Lee
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  3. Jungsik Lee
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  4. Mingi Park
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  5. Malrey Lee
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Editor information

Editors and Affiliations

  1. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  2. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  5. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  6. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

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© 2004 Springer-Verlag Berlin Heidelberg

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Gerardo, B.D., Lee, J., Lee, J., Park, M., Lee, M. (2004). The Association Rule Algorithm with Missing Data in Data Mining. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3043. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24707-4_13

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  • DOI: https://doi.org/10.1007/978-3-540-24707-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22054-1

  • Online ISBN: 978-3-540-24707-4

  • eBook Packages: Springer Book Archive

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