Discovering Quasi-Periodic-Frequent Patterns in Transactional Databases

  • R. Uday Kiran
  • Masaru Kitsuregawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8302)


Periodic-frequent patterns are an important class of user-interest-based frequent patterns that exist in a transactional database. A frequent pattern can be said periodic-frequent if it appears periodically throughout the database. We have observed that it is difficult to mine periodic-frequent patterns in very large databases. The reason is that the occurrence behavior of the patterns can vary over a period of time causing periodically occurring patterns to be non-periodic and/or vice-versa. We call this problem as the “intermittence problem.” Furthermore, in some of the real-world applications, the users may be interested in only those frequent patterns that might have appeared almost periodically throughout the database. With this motivation, we relax the constraint that a pattern must appear periodically throughout the database, and introduce a new class of user-interest-based frequent patterns, called quasi-periodic-frequent patterns. Informally, a frequent pattern is said to be quasi-periodic-frequent if most of its occurrences are periodic in a database. We propose a model and a pattern-growth algorithm to discover these patterns. The proposed patterns do not satisfy the downward closure property. We have introduced three pruning techniques to reduce the computational cost of mining the patterns. Experimental results show that the proposed patterns can provide useful information and the proposed algorithm is efficient.


Data mining knowledge discovery in databases frequent patterns and periodic behavior 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Frequent itemset mining repository,
  2. 2.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD, pp. 207–216 (1993)Google Scholar
  3. 3.
    Antunes, C.M., Oliveira, A.L.: Temporal data mining: An overview. In: Workshop on Temporal Data Mining, KDD (2001)Google Scholar
  4. 4.
    Aref, W.G., Elfeky, M.G., Elmagarmid, A.K.: Incremental, online, and merge mining of partial periodic patterns in time-series databases. IEEE TKDE 16(3), 332–342 (2004)Google Scholar
  5. 5.
    Cheng, J., Ke, Y., Ng, W.: A survey on algorithms for mining frequent itemsets over data streams. Knowledge and Information Systems 16(1), 1–27 (2008)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Han, J., Dong, G., Yin, Y.: Efficient mining of partial periodic patterns in time series database. In: ICDE, pp. 106–115 (1999)Google Scholar
  7. 7.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Uday Kiran, R., Krishna Reddy, P.: Towards efficient mining of periodic-frequent patterns in transactional databases. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010, Part II. LNCS, vol. 6262, pp. 194–208. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Kiran, R.U., Reddy, P.K.: An alternative interestingness measure for mining periodic-frequent patterns. In: Yu, J.X., Kim, M.H., Unland, R. (eds.) DASFAA 2011, Part I. LNCS, vol. 6587, pp. 183–192. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Li, C., Yang, Q., Wang, J., Li, M.: Efficient mining of gap-constrained subsequences and its various applications. ACM Trans. Knowl. Discov. Data 6(1), 2:1–2:39 (2012)Google Scholar
  11. 11.
    Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects. In: KDD 2010, pp. 1099–1108 (2010)Google Scholar
  12. 12.
    Ma, S., Hellerstein, J.: Mining partially periodic event patterns with unknown periods. In: ICDE, pp. 205–214 (2001)Google Scholar
  13. 13.
    Özden, B., Ramaswamy, S., Silberschatz, A.: Cyclic association rules. In: ICDE, pp. 412–421. IEEE Computer Society, Washington, DC (1998)Google Scholar
  14. 14.
    Rashid, M. M., Karim, M. R., Jeong, B.-S., Choi, H.-J.: Efficient mining regularly frequent patterns in transactional databases. In: Lee, S.-G., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012, Part I. LNCS, vol. 7238, pp. 258–271. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Sheng, C., Hsu, W., Lee, M.L.: Mining dense periodic patterns in time series data. In: ICDE, Washington, DC, USA, pp. 115–117 (2006)Google Scholar
  16. 16.
    Tanbeer, S.K., Ahmed, C.F., Jeong, B.-S., Lee, Y.-K.: Discovering periodic-frequent patterns in transactional databases. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 242–253. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Yang, J., Wang, W., Yu, P.S.: Mining asynchronous periodic patterns in time series data. IEEE Trans. on Knowl. and Data Eng. 15(3), 613–628 (2003)CrossRefGoogle Scholar
  18. 18.
    Yang, R., Wang, W., Yu, P.: Infominer+: mining partial periodic patterns with gap penalties. In: ICDM, pp. 725–728 (2002)Google Scholar
  19. 19.
    Zhang, M., Kao, B., Cheung, D.W., Yip, K.Y.: Mining periodic patterns with gap requirement from sequences. ACM Trans. Knowl. Discov. Data 1(2) (August 2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • R. Uday Kiran
    • 1
  • Masaru Kitsuregawa
    • 1
  1. 1.Institute of Industrial ScienceThe University of TokyoTokyoJapan

Personalised recommendations