Identifying Calendar-Based Periodic Patterns

  • Jhimli Adhikari
  • P. R. Rao
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 13)


A large class of problems deals with temporal data. Identifying temporal patterns in these datasets is a natural as well as an important task. In the recent time, researchers have reported an algorithm for finding calendar-based periodic pattern in a time-stamped data and introduced the concept of certainty factor in association with an overlapped interval. In this paper, we have extended the concept of certainty factor by incorporating support information for effective analysis of overlapped intervals. We have proposed a number of improvements in the algorithm for identifying calendar-based periodic patterns. In this direction we have proposed a hash based data structure for storing and managing patterns. Based on our modified algorithm, we identify full as well as partial periodic calendar-based patterns. We provide a detailed data analysis incorporating various parameters of the algorithm and make a comparative analysis with the existing algorithm, and show the effectiveness of our algorithm. Experimental results are provided on both real and synthetic databases.


Calendar-based pattern Certainty factor Overlapped interval Periodic pattern Temporal pattern Time-stamped database 


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  1. 1.
    Adhikari, A., Rao, P.R.: A framework for mining arbitrary Boolean expressions induced by frequent itemsets. In: Proceedings of the International Conference on Artificial Intelligence, pp. 5–23 (2007)Google Scholar
  2. 2.
    Adhikari, A., Rao, P.R.: Mining conditional patterns in a database. Pattern Recognition Letters 29(10), 1515–1523 (2008)CrossRefGoogle Scholar
  3. 3.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD Conference Management of Data, pp. 207–216 (1993)Google Scholar
  4. 4.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th Very Large databases (VLDB) Conference, pp. 487–499 (1994)Google Scholar
  5. 5.
    Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of International Conference on Data Engineering (ICDE), pp. 3–14 (1995)Google Scholar
  6. 6.
    Ale, J.M., Rossi, G.H.: An approach to discovering temporal association rules. In: Proceedings of ACM Symposium on Applied Computing, pp. 294–300 (2000)Google Scholar
  7. 7.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Communications of the ACM 26(11), 832–843 (1983)MATHCrossRefGoogle Scholar
  8. 8.
    Baruah, H.K.: Set superimposition and its application to the theory of fuzzy sets. J. Assam Science Soc. 10(1 and 2), 25–31 (1999)Google Scholar
  9. 9.
    Bettini, C., Jajodia, S., Wang, S.X.: Time Granularities in Databases. In: Data Mining and Temporal Reasoning. Springer (2000)Google Scholar
  10. 10.
    Frequent itemset mining dataset repository,
  11. 11.
    Han, J., Dong, G., Yin, Y.: Efficient mining on partial periodic patterns in time series database. In: Proceedings of Fifteenth International Conference on Data Engineering, pp. 106–115 (1999)Google Scholar
  12. 12.
    Hong, T.P., Wu, Y.Y., Wang, S.L.: An effective mining approach for up-to-date patterns. Expert Systems with Applications (36), 9747–9752 (2009)CrossRefGoogle Scholar
  13. 13.
    Kempe, S., Hipp, J., Lanquillon, C., Kruse, R.: Mining frequent temporal patterns in interval sequences. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16(5), 645–661 (2008)MATHCrossRefGoogle Scholar
  14. 14.
    Lee, J.W., Lee, Y.J., Kim, H.K., Hwang, B.H., Ryu, K.H.: Discovering temporal relation rules mining from interval data. In: Proceedings of the 1st Euro Asian Conference on Advance in Information and Communication Technology, Iran, (2002)Google Scholar
  15. 15.
    Lee, Y.J., Lee, J.W., Chai, D., Hwang, B., Ryu, K.H.: Mining temporal interval relational rules from temporal data. Journal of Systems and Software 82(1), 155–167 (2009)CrossRefGoogle Scholar
  16. 16.
    Lee, G., Yang, W., Lee, J.M.: A parallel algorithm for mining multiple partial periodic patterns. Information Science 176(24), 3591–3609 (2006)MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    Li, D., Deogun, J.S.: Discovering Partial Periodic Sequential Association Rules with Time Lag in Multiple Sequences for Prediction. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 332–341. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Li, Y., Ning, P., Wang, X.S., Jajodia, S.: Discovering calendar-based temporal association rules. Data and Knowledge Engineering 44(2), 193–218 (2003)CrossRefGoogle Scholar
  19. 19.
    Mahanta, A.K., Mazarbhuiya, F.A., Baruah, H.K.: Finding Locally and Periodically Frequent Sets and Periodic Association Rules. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 576–582. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  20. 20.
    Mahanta, A.K., Mazarbhuiya, F.A., Baruah, H.K.: Finding calendar-based periodic patterns. Pattern Recognition Letters 29(9), 1274–1284 (2008)CrossRefGoogle Scholar
  21. 21.
    Ozden, B., Ramaswamy, S., Silberschatz, A.: Cyclic association rules. In: Proceedings of 14th International Conference on Data Engineering, pp. 412–421 (1998)Google Scholar
  22. 22.
    Roddick, J.F., Spiliopoulou, M.: A survey of temporal knowledge discovery paradigms and methods. In: IEEE TKDE, pp. 750–767 (2002)Google Scholar
  23. 23.
    Verma, K., Vyas, O.P., Vyas, R.: Temporal Approach to Association Rule Mining Using T-Tree and P-Tree. In: Perner, P., Imiya, A. (eds.) MLDM 2005. LNCS (LNAI), vol. 3587, pp. 651–659. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  24. 24.
    Wu, X., Zhang, C., Zhang, S.: Efficient mining of both positive and negative association rules. ACM Transactions on Information Systems 22(3), 381–405 (2004)CrossRefGoogle Scholar
  25. 25.
    Zimbrao, G., de Souza, J.M., de Almeida, V.T., de Silva, W.A.: An algorithm to discover calendar-based temporal association rules with item’s lifespan restriction. In: Proceedings of 2nd Workshop on Temporal Data Mining (2002)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceNarayan Zantye CollegeBicholimIndia
  2. 2.Department of Computer Science and TechnologyGoa UniversityBicholimIndia

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