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Incremental Updating Algorithm of Weighted Negative Association Rules

  • He Jiang
  • Wenqing Lei
Part of the Communications in Computer and Information Science book series (CCIS, volume 392)

Abstract

Incremental updating algorithm for mining negative association rules is different from positive association rules mining. With the continued increase of the data records in the database, incremental updating association rules technique represent an important class of knowledge that can be discovered from data warehouses. Incremental updating algorithm is important for mining infrequent item sets in dynamic databases. In this paper, we proposed an incremental updating algorithm of weighted negative association rules (WNARI). By comparing weighted itemsets and unweighted case, the number of incremental updating negative association rules on weighted condition is less than the unweighted.

Keywords

Negative Association Rule Weight Incremental Updating Infrequent Item Set 

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References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, vol. 11(3), pp. 207–216. ACM Press, New York (1993)CrossRefGoogle Scholar
  2. 2.
    Cai, C.H., Fu, A.W.-C., Cheng, C.H., et al.: Mining Association Rules with Weighted Items. In: Proceedings of the International Database Engineering and Application Symposium, Cardiff, UK, vol. 13(2), pp. 68–77 (1998)Google Scholar
  3. 3.
    Sun, B.Y., Jiang, H.: Incremental updating algorithm for mining association rules. Computer Engineering 5(6), 676–677 (2009)Google Scholar
  4. 4.
    Zhao, Y.Y., Jiang, H.: Research of mining positive and negative weighted association rules based on Chi-squared analysis. In: Proceeding of The Second International Conference on Information and Computing Science Manchester, vol. 14(11), pp. 234–238 (2009)Google Scholar
  5. 5.
    Jiang, H., Zhao, Y.Y., Dong, X.J.: Mining both Positive and Negative Weighted Association Rules with Multiple Minimum Supports. In: Proceeding of 2008 International Conference on Computer Science and Software Engineering, China, vol. 24(22), pp. 407–410 (2008)Google Scholar
  6. 6.
    Zhao, Y.Y., Jiang, H.: Mining Weighted Negative Association Rules Based on Correlation from Infrequent Items. In: Proceeding of 2009 International Conference on Advanced Computer Control, Singapore, vol. 15(5), pp. 270–273 (2009)Google Scholar
  7. 7.
    Jiang, H., Luan, X.M., Liu, G.L., Dong, X.J.: Mining Negative Weighted Association Rules from Infrequent Itemsets Based on Multiple Supports. American Journal of Engineering and Technology Research 11(9), 3848–3854 (2011)Google Scholar
  8. 8.
    Jiang, H., Dong, W.J.: Algorithms for Pruning Weighted Negative Association Rules. In: Proceeding of 2011 4th IEEE International Conference on Computer Science and Information Technology, China, vol. 53(9), pp. 336–340 (2011)Google Scholar
  9. 9.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th Int’l Conference on VLDB, vol. 24(6), pp. 487–499 (1994)Google Scholar
  10. 10.
    Brin, S., Montwani, R., Silverstein, C.: Beyond Market Baskets: Generalizing Association Rules to Correlations. In: Proceedings for the ACM SIGMOD Conference, vol. 24(10), pp. 265–276 (1997)Google Scholar
  11. 11.
    Feng, Y.-C., Feng, J.-L.: Incremental Updating Algorithm for Mining Association Rules. Journal of Software 9(4), 301–306 (1998)Google Scholar
  12. 12.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, September 12-15, pp. 487–499 (1994)Google Scholar
  13. 13.
    Berzal, F., Cubero, J.-C., Marín, N., Serrano, J.-M.: TBAR: An efficient method for association rule mining in relational databases. Data & Knowledge Engineering 37(1), 47–64 (2001)CrossRefMATHGoogle Scholar
  14. 14.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, Tucson, Arizona, United States, May 11-15, pp. 255–264 (1997)Google Scholar
  15. 15.
    Calders, T., Goethals, B.: Mining all non-derivable frequent itemsets. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 74–85. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Han, J., Fu, Y.: Discovery of Multiple-Level Association Rules from Large Databases. In: Proceedings of the 21st International Conference on Very Large Data Bases, September 11-15, pp. 420–431 (1995)Google Scholar
  17. 17.
    Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: Proceedings of the fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, United States, August 15-18, pp. 337–341 (1999)Google Scholar
  18. 18.
    Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Efficient mining of association rules using closed itemset lattices. Information Systems 24(1), 25–46 (1999)CrossRefGoogle Scholar
  19. 19.
    Ramkumar, G.D., Ranka, S., Tsur, S.: Weighted Association Rules: Model and Algorithm. In: KDD 1998 (1998)Google Scholar
  20. 20.
    Tao, F.: Mining Binary Relationships from Transaction Data in Weighted Settings. PhD Thesis, School of Computer Science, Queen’s University Belfast, UK (2003)Google Scholar
  21. 21.
    Wang, W., Yang, J., Yu, P.S.: Efficient mining of weighted association rules (WAR). In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, Massachusetts, United States, August 20-23, pp. 270–274 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • He Jiang
    • 1
  • Wenqing Lei
    • 2
  1. 1.School of InformationShandong Polytechnic UniversityJinanChina
  2. 2.School of Information EngineeringShandong Youth University of Political ScienceJinanChina

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