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An enhanced incremental association rule discovery with a lower minimum support

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Abstract

In the real world of data, a new set of data has been being inserted into the existing database. Thus, the rule maintenance of association rule discovery in large databases is an important problem. Every time the new data set is appended to an original database, the old rule may probably be valid or invalid. This paper proposed the approach to calculate the lower minimum support for collecting the expected frequent itemsets. The concept idea is applying the normal approximation to the binomial theory. This proposed idea can reduce a process of calculating probability value for all itemsets that are unnecessary. In addition, the confidence interval is also applied to ensure that the collection of expected frequent itemsets is properly kept.

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References

  1. Agrawal R, Imielinski T, Swanmi A (1993) A mining association rules between sets of items in large databases. In: Proceeding of the ACM SIGMOD Int’l Conference on Management of Data (ACM SIGMOD’93), Washington, USA, May 1993, pp 207–216

  2. Agrawal R, Srikant R (1994) Fast algorithm for mining association rules. In: Proceedings 20th International Conference on Very large Databases (VLDB’94), Santiago, Chile, September 12–15, 1994, pp 487–499

  3. Cheng DW, Han J, Ng VT, Wong CY Maintenance of Discovered association rules in large databases: an incremental updating technique. In: 12th IEEE International Conference on Data Engineering, 1996, pp 106–114

  4. Toivonen H, Sampling large database for association rules. In: Proceedings of the 22th International Conference on Very Large Database (VLDB’96), September 1996, pp 134–145

  5. Amornchewin R, Kreesuradej W (2009) Mining dynamic databases using probability-based incremental association rule discovery algorithm. J Univers Comput Sci 15(12):2409–2428

    Google Scholar 

  6. Teng WG, Chen MS (2005) Incremental mining on association rules, vol 180. Springer, Berlin, pp 125–162

    MATH  Google Scholar 

  7. Lee CH, Lin CR, Chen MS (2001) Sliding-window filtering: an efficient algorithm for incremental mining. In: Proceeding of the ACM 10th International Conference on Information and Knowledge Management, November 2001

  8. Chang CH, Yang SH Enhancing SWF for incremental association mining by itemset maintenance. In: Proceedings of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, April 2003

  9. Ezeife EI, Su Y Mining incremental association rules with generalized FP-tree. In: Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence, May 2002, pp 147–160

  10. Pradeepini G, Jyothi S Tree-based incremental association rule mining without candidate itemset generation. In: 2nd International Conference on Trendz in Information Sciences and Computing (TISC2010), December 17–19, 2010, pp 78–81

  11. TP Le, TP Hong, B Vo, B Le An Efficient incremental mining approach based on IT-tree. In: 2012 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future, February 27–March 1, 2012

  12. Hong TP, Wang CY, Tao YH (2001) A new incremental data mining algorithm using pre-large itemsets. J Intell Data Anal 5(2):111–129

    MATH  Google Scholar 

  13. Tsai PSM, Lee CC, Chen ALP An efficient approach for incremental association rule mining. In: Proceedings of the third Pacific-Asia Conference on methodologies for Knowledge discovery and Data Mining, Lecture notes in computer Science, Vol. 1574 archive, 1999

  14. Robert VH, Elliot AT (2010) Probability and statistical inference, 8th edn. Pearson Education Inc, New Jersey, pp 78–86

    Google Scholar 

  15. Larry JK (1998) Exploring statistics: a modern introduction to data analysis and inference, 2nd edn. Cengage Learning, US, pp 265–275

  16. John EF, Benjamin MP (2004) Statistics: a first course, 8th edn. Pearson Education Inc, New Jersey, pp 256–260

    Google Scholar 

  17. Tafeng groceries shopping dataset. http://recsyswiki.com/wiki/Grocery_shopping_datasets. Accessed 15 May 2016

Download references

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Authors and Affiliations

  1. Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520, Thailand

    Araya Ariya & Worapoj Kreesuradej

Authors
  1. Araya Ariya
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  2. Worapoj Kreesuradej
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Correspondence to Araya Ariya.

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Cite this article

Ariya, A., Kreesuradej, W. An enhanced incremental association rule discovery with a lower minimum support. Artif Life Robotics 21, 466–477 (2016). https://doi.org/10.1007/s10015-016-0288-3

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  • DOI: https://doi.org/10.1007/s10015-016-0288-3

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