Advertisement

Hybrid Temporal Mining for Finding Out Frequent Itemsets in Temporal Databases Using Clustering and Bit Vector Methods

  • M. Krishnamurthy
  • A. Kannan
  • R. Baskaran
  • G. Bhuvaneswari
Part of the Communications in Computer and Information Science book series (CCIS, volume 141)

Abstract

Hybrid Temporal Pattern Mining was designed to address the problem of discovering frequent patterns of point and interval-based events or both and it is essential in many applications, including market analysis, decision support and business management. Such methodology cannot deal with Clustering, Bit Vector and Variable Threshold. In this paper, we propose a new algorithm called RHTPM (Revised Hybrid Temporal Pattern Mining) to find the frequent temporal pattern based on Clustering, Bit Vector and Variable Threshold. The experiments demonstrate that the proposed algorithm is capable of mining frequent hybrid temporal pattern for effective decision making and has been proved to be significantly good.

Keywords

Hybrid temporal pattern point and interval-based events clustering bit vector variable threshold 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proceedings of the SIGMOD Conference, pp. 207–216 (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the International Conference of Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  3. 3.
    Prasetyo, B., Pramudiono, I., Kitsuregawa, M.: Hmine-rev: Toward H-mine Parallelization on Mining Frequent Patterns in Large Databases. In: Proceedings of the IEIC Technical Report (Institute of Electronics, Information and Communication Engineers), pp. 49–54 (2005)Google Scholar
  4. 4.
    Bettini, C., Jajodia, S.G., Wang, S.X.: Time Granularities in Databases, Data Mining, and Temporal reasoning, p. 230. Springer, New York (2000) ISBN 3-540-66997-3CrossRefzbMATHGoogle Scholar
  5. 5.
    Han, J., Pei, J., Yin, Y.: Mining Frequent Pattern without Candidate Generation. In: Proceedings of the ACM SIGMOD Conference on Management of Data, pp. 487–499 (2000)Google Scholar
  6. 6.
    Ale, J.M., Rossi, G.H.: An Approach to Discovering Temporal Association Rules. In: Proceedings of the 2000 ACM Symposium on Applied Computing, vol. 1, pp. 294–300 (2002)Google Scholar
  7. 7.
    Verma, K., Vyas, O.P.: Efficient Calendar Based Temporal Association Rule. Proceedings of the SIGMOD record 34(3), 63–70 (2005)CrossRefGoogle Scholar
  8. 8.
    Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases. In: Proceedings of the International Conference on Data Mining (ICDM 2001), pp. 441–448 (2001)Google Scholar
  9. 9.
    Liu, C., An, J.: Fast Mining and Updating Frequent Itemsets. In: Proceedings of the 2008 International Colloquium on Computing, Communication, Control and Management (ISECS 2008), pp. 365–368 (2008)Google Scholar
  10. 10.
    Wu, S.-Y., Chen, Y.-L.: Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events. In: Proceedings of the Conference on Privacy in Statistical Databases, vol. 68(11), pp. 1309–1330 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. Krishnamurthy
    • 1
  • A. Kannan
    • 2
  • R. Baskaran
    • 3
  • G. Bhuvaneswari
    • 4
  1. 1.Department of Computer ApplicationsSri Venkateswara College of EngineeringSriperumbudurIndia
  2. 2.Department of Information Science and TechnologyAnna UniversityChennaiIndia
  3. 3.Department of Computer Science and EngineeringAnna UniversityChennaiIndia
  4. 4.Sri Venkateswara College of EngineeringSriperumbudurIndia

Personalised recommendations