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Novel and Efficient Hybrid Strategies for Constraining the Search Space in Frequent Itemset Mining

  • B. Kalpana
  • R. Nadarajan
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 6)

Association rule mining was originally applied in market basket analysis which aims at understanding the behaviour and shopping preferences of retail customers. The knowledge is used in product placement, marketing campaigns, and sales promotions. In addition to the retail sector, the market basket analysis framework is also being extended to the health and other service sectors. The application of association rule mining now extends far beyond market basket analysis and includes detection of network intrusions, attacks from Web server logs, and prediciting user traversal patterns on the Web.

Keywords

Frequent Itemsets Hybrid Strategy Frequent Itemset Mining Sample Database Dense Dataset 
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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    Shenoy, P. et al. (2000). Turbo charging vertical mining of large databases, International Conference on Management of Data.Google Scholar
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    Zaki, M.J. (2000). Scalable algorithms for association mining, IEEE Transactions on Knowledge and Data Engineering, vol. 12, no. 3, pp. 372–390.CrossRefMathSciNetGoogle Scholar
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    Zaki, M.J. and Gouda, K. (2003). Fast vertical mining using diffsets, SIGKDD’.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • B. Kalpana
    • 1
  • R. Nadarajan
    • 2
  1. 1.Department of Computer ScienceAvinashilingam University for WomenCoimbatoreIndia
  2. 2.Department of Mathematics and Computer ApplicationsPSG College of TechnologyCoimbatoreIndia

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