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An Algorithm for Mining Association Rules Based on the Database Characteristic Matrix

  • YU Tong
  • XU Meide
Conference paper

Abstract

This paper proposes a new algorithm for mining association rules. In order to calculate itemsets support, this paper puts forward the concept of database characteristic matrix and characteristic vector, and emerges an algorithm for mining association rules based on the characteristic matrix. This algorithm needs to traverse the database one time only, and the database operation has been reduced greatly. Based on the characteristic vector inner product, an itemset support can be obtained and the efficiency of the algorithm has been improved.

Keywords

Association rules Data mining Database characteristic matrix Database traversal 

References

  1. 1.
    Mannila H, Toivonen H, Verkamo A (1994) Efficient algorithm for discovering association rules. In: AAAI Workshop on knowledge discovery in databases, pp 181–192Google Scholar
  2. 2.
    Srikant R, Agrawal R (1995) Mining generalized association rulers. In: Proceedings of the 21th international conference on very large database, pp 407–419Google Scholar
  3. 3.
    Toivonen H, Klemettinen M, Ronkaine P et al (1995) Pruning and grouping discovery association rules. In: Mlnet workshop on statistics, machine learning and discovery in database, Herakloon, Crete, GreeceGoogle Scholar
  4. 4.
    Agrawal R, Srikant R (1994) Fast algorithms for mining. Association rules in large databases. In: Proceedings 20th international conference very large databases, pp 478–499Google Scholar
  5. 5.
    Spark J (ed) (1995) An effective hash based algorithm for mining association rules. In: Proceedings ACM SIGMOD, pp 175–186Google Scholar
  6. 6.
    Toivonen H (1996) Sampling large databases for association rules. In: Proceedings of the 22th international conference on Very Large Databases (VLDB), Bombay, india. Morgan Kaufmann, pp 134–145Google Scholar
  7. 7.
    Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD conference on management of data, pp 207–216Google Scholar
  8. 8.
    Wang NB (2000) Database management system. Publishing House of electronics industry, Beijing, pp 435–439Google Scholar
  9. 9.
    Jiawei H (2001) Micheline, translated by Meng xiao feng. Data mining-concepts and techniques. Machinery Industry Press, p 8Google Scholar
  10. 10.
    Zhong Zhi S (2002) Knowledge discovery. Tsinghua University press, p 1Google Scholar
  11. 11.
    Ye Xin T, Qi C, Rui Zhao Y (2000) Survey of association rule mining. Appl Res Comput, p 1Google Scholar

Copyright information

© Atlantis Press and the author(s) 2016

Authors and Affiliations

  1. 1.Automation Engineering InstituteBeijing PolytechnicBeijingChina

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