Frequent Itemsets and Association Rules with a Certain Probability in Data Mining

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 217)

Abstract

Data mining, referred to as knowledge discovery in databases, is the extraction of patterns representing valuable knowledge implicitly stored in large databases or data warehouses, and the regional data mining research is quite limited. We acquired information from our regional data mining is based on the data mining with probability. First, the new algorithm is presented. Second, we calculated the support and the confidence by the numeric parsers. Finally, we propose a region-based location strategy and tested the conclusion.

Keywords

Frequent itemsets Association rules Open education 

Notes

Acknowledgments

This works supported by Chongqing Radio and Tv University project (No: 2011YY002) and Chongqing educational reform project (No: 113278).

References

  1. 1.
    Han J, Kambe M (2007) Data mining concepts and techniques, Fanming, Meng xiaofen translation, vol 8. Mechanical Industry Press, Beijing, pp 43–49Google Scholar
  2. 2.
    Li C-X, Zhao L (2010) Improved incremental mining algorithm. Comput Eng 36(24):112–116Google Scholar
  3. 3.
    Liu H-M (2008) The association rule application in the students’ score. J Lincang Teachers’ Coll 5(4):26–29Google Scholar
  4. 4.
    Chen X-L, Li H, Zhang Z (2008) The association rule application in the students’ credit management. J Inf Tech Appl 29:8–12Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Chongqing Radio and Tv UniversityChongqingChina

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