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An Efficient Approach to Mine Periodic-Frequent Patterns in Transactional Databases

  • Akshat Surana
  • R. Uday Kiran
  • P. Krishna Reddy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7104)

Abstract

Recently, temporal occurrences of the frequent patterns in a transactional database has been exploited as an interestingness criterion to discover a class of user-interest-based frequent patterns, called periodic-frequent patterns. Informally, a frequent pattern is said to be periodic-frequent if it occurs at regular intervals specified by the user throughout the database. The basic model of periodic-frequent patterns is based on the notion of “single constraints.” The use of this model to mine periodic-frequent patterns containing both frequent and rare items leads to a dilemma called the “rare item problem.” To confront the problem, an alternative model based on the notion of “multiple constraints” has been proposed in the literature. The periodic-frequent patterns discovered with this model do not satisfy downward closure property. As a result, it is computationally expensive to mine periodic-frequent patterns with the model. Furthermore, it has been observed that this model still generates some uninteresting patterns as periodic-frequent patterns. With this motivation, we propose an efficient model based on the notion of “multiple constraints.” The periodic-frequent patterns discovered with this model satisfy downward closure property. Hence, periodic-frequent patterns can be efficiently discovered. A pattern-growth algorithm has also been discussed for the proposed model. Experimental results show that the proposed model is effective.

Keywords

Data mining frequent patterns and rare item problem 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Akshat Surana
    • 1
  • R. Uday Kiran
    • 1
  • P. Krishna Reddy
    • 1
  1. 1.Center for Data EngineeringInternational Institute of Information Technology-HyderabadHyderabadIndia

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