Finding Locally and Periodically Frequent Sets and Periodic Association Rules

  • A. Kakoti Mahanta
  • F. A. Mazarbhuiya
  • H. K. Baruah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

The problem of finding association rules from a dataset is to find all possible associations that hold among the items, given a minimum support and confidence. This involves finding frequent sets first and then the association rules that hold within the items in the frequent sets. In temporal datasets as the time in which a transaction takes place is important we may find sets of items that are frequent in certain time intervals but not frequent throughout the dataset. These frequent sets may give rise to interesting rules but these can not be discovered if we calculate the supports of the item sets in the usual way. We call here these frequent sets locally frequent. Normally these locally frequent sets are periodic in nature. We propose modification to the Apriori algorithm to compute locally frequent sets and periodic frequent sets and periodic association rules.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD 1993, Washington (1993)Google Scholar
  2. 2.
    Ale, J.M., Rossi, G.H.: An approach to discovering temporal association rules. In: Proceedings of 2000 ACM symposium on Applied Computing (2000)Google Scholar
  3. 3.
    Chen, X., Petrounias, I.: A framework for Temporal Data Mining. In: Quirchmayr, G., Bench-Capon, T.J.M., Schweighofer, E. (eds.) DEXA 1998. LNCS, vol. 1460, pp. 796–805. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  4. 4.
    Chen, X., Petrounias, I.: Language support for Temporal Data Mining. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 282–290. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Chen, X., Petrounias, I., Healthfield, H.: Discovering temporal Association rules in temporal databases. In: Proceedings of IADT 1998, pp. 312–319 (1998)Google Scholar
  6. 6.
    Li, Y., Ning, P., Wang, X.S., Jajodia, S.: Discovering Calendar-based Temporal Association Rules. In: Proc. of the 8th Int’l Symposium on Temporal Representation and Reasonong (2001)Google Scholar
  7. 7.
    Ozden, B., Ramaswamy, S., Silberschatz, A.: Cyclic Association Rules. In: Proc. of the 14th Int’l Conference on Data Engineering, USA, pp. 412–421 (1998)Google Scholar
  8. 8.
    Zimbrao, G., Moreira de Souza, J., Teixeira de Almeida, V., Araujo da Silva, W.: An Algorithm to Discover Calendar-based Temporal Association Rules with Item’s Lifespan Restriction. In: Proc. of the 8th ACM SIGKDD 2002 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • A. Kakoti Mahanta
    • 1
  • F. A. Mazarbhuiya
    • 1
  • H. K. Baruah
    • 2
  1. 1.Department of Computer ScienceGauhati UniversityIndia
  2. 2.Department of StatisticsGauhati University 

Personalised recommendations