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Iterated Data Mining Techniques on Embedded Vector Modeling

Conference paper
Part of the The IMA Volumes in Mathematics and its Application book series (IMA, volume 132)

Abstract

Classification and prediction problems of transaction databases are well known data-mining problems. Their importance becomes further noticeable at the time of information data explosion. The existence of high volume transactional data provides us the challenge of exploring the valuable and meaningful hidden information. The classic approaches, such as association analysis and cluster analysis, always request human interventions either in predefining the logical correlations or in setting the weights for attribute parameters ([HK]). Also in the most cases the binary results are presented with prematurely chosen thresholds.

Keywords

Match Rate User Space Action Item Transaction Database Space Embedding 
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

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    R. Agrawal and J. Shafer, Parallel mining of association rules, IEEE Transactions on Knowledge and Data Engineering, 8(6): 962–969, 1996.CrossRefGoogle Scholar
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    K. Cios, W. Pedrycz, and R. Swiniarski, Data Mining Methods for Knowledge Discovery, Kluwer Academic Publishers, 1998.MATHCrossRefGoogle Scholar
  3. [HK]
    J. Han and M. Kamber, Data Mining — Concepts and Technique, Morgan Kaufmann, 2001.Google Scholar
  4. [Lu]
    N. Lu, Fractal Imaging, Academic Press, 1997.MATHGoogle Scholar
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    K. William (ed.), Professional XML Database, Wrox Press, 2000.Google Scholar

Copyright information

© Springer-Verlag New York, Inc. 2002

Authors and Affiliations

  • Ning Lu
    • 1
  1. 1.CTOConveigh, Inc.Redwood CityUSA

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