Inducing Fuzzy Association Rules with Multiple Minimum Supports for Time Series Data

  • Rakesh Rathi
  • Vinesh Jain
  • Anshuman Kumar Gautam
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)


Technological changes have occurred at an exponential rate in recent years leading to the generation of large amount of data in various sectors. Several database and data warehouse is built to store and manage the data. As we know the data which are relevant to us should be extracted from the database for our task. Earlier different mining approaches are proposed in which items are collected at same minimum support value. In this paper we propose a fuzzy data mining algorithm which generates the fuzzy association rules from time series data having different minimum support values. The temperature varying dataset is used to generate fuzzy rules. The proposed algorithm also predicts the variation of temperature. Experiments are also performed to get the desired result.


Association rule Data mining Different minimum support Fuzzy set 


  1. 1.
    Aggrawal, R.: Mining association rules between sets of items inlarge database ACMGoogle Scholar
  2. 2.
    Stepnicka, M.: Time series analysis and prediction based on fuzzy rules and the fuzzy transformGoogle Scholar
  3. 3.
    Dr.Sivatsa S.K.: Inaccuracy minimization by partitioning fuzzy data sets—validation of an analytical methodology(IJCSIS). Int. J. Comput. Sci. Inf. Secur. 8(1), (2010)Google Scholar
  4. 4.
    Das, G.: Rule discovery from time series. In: Proceedings of the 4 the International ConferenceGoogle Scholar
  5. 5.
    Pongracz, R.: Application of fuzzy rule-based modeling technique to regional drought. J. Hydrol. 224, 100–114 (1999)CrossRefGoogle Scholar
  6. 6.
    Mueen, A.A.: Exact primitives for time series data mining. University of California, Riverside (2012)Google Scholar
  7. 7.
    Zhu, Y.: High performance data mining in time series: techniques and case studies. New work University, New York (2004)Google Scholar
  8. 8.
    Herrera, Francisco: Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets Syst. 160, 905–921 (2009)CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Han, J.: Data Mining concepts and TechniquesGoogle Scholar
  10. 10.
    Hong, T.P.: Mining association rules with multiple minimum supports. Int. J. Approximate reasoning. 3, 38–42 (2005)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • Rakesh Rathi
    • 1
  • Vinesh Jain
    • 1
  • Anshuman Kumar Gautam
    • 1
  1. 1.Government Engineering College AjmerAjmerIndia

Personalised recommendations