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)


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.


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


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© 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

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