Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Frequent Partial Orders

  • Antti Ukkonen
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_172

Definition

Given a set D of n partial orders on S, and a threshold σn, a partial order P is a frequent partial order (FPO) if it is compatible with more than σ partial in D. Typically D contains total orders either on S or arbitrary subsets of S.

Historical Background

A natural extension of association rule mining is to make use of temporal information. This was first done in [1], where the authors present algorithms for mining frequently occurring sequences of sets of items in a database of transactions. Each of such sequences can be seen as a partial order on the complete set of items. For more recent work on the same topic please see [13, 8, 12]. The slightly different problem of mining frequent episodes from a sequence of events is presented in [7]. In this case an episode is a partial order over the set of all possible events. The problem differs from the one of [1] by considering a stream of events (for example notifications and alerts generated by devices in a...

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Recommended Reading

  1. 1.
    Agrawal R, Srikant R. Mining sequential patterns. In: Proceedings of the 11th International Conference on Data Engineering; 1995. p. 3–14.Google Scholar
  2. 2.
    Ben-Dor A, Chor B, Karp R, Yakhini Z. Discovering local structure in gene-expression data: the order preserving submatrix problem. In: Proceedings of the 6th Annual International Conference on Computational Biology; 2002. p. 49–57.Google Scholar
  3. 3.
    Fernandez PL, Heath LS, Ramakrishnan N, Vergara JP. Reconstructing partial orders from linear extensions. In: Proceedings of the 4th SIGKDD Workshop on Temporal Data Mining: Network Reconstruction from Dynamic Data; 2006.Google Scholar
  4. 4.
    Gwadera R, Atallah MJ, Szpankowski W. Reliable detection of episodes in event sequences. In: Proceedings of the 3rd IEEE International Conference on Data Mining; 2003. p. 67–74.Google Scholar
  5. 5.
    Laxman S, Sastry PS, Unnikrishnan KP. A fast algorithm for finding frequent episodes in event streams. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2007. p. 410–9.Google Scholar
  6. 6.
    Mannila H, Meek C. Global partial orders from sequential data. In: Proceedings of the 6th ACM SIGKDD International Conferenec on Knowledge Discovery and Data Mining; 2000. p. 161–8.Google Scholar
  7. 7.
    Mannila H, Toivonen H, Verkamo I. Discovering frequent episodes in sequences. In: Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining; 1995. p. 210–5.Google Scholar
  8. 8.
    Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu M-C. PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the 17th International Conference on Data Engineering; 2001. p. 215–24.Google Scholar
  9. 9.
    Pei J, Liu J, Wang H, Wang K, Yu PS, Wang J. Efficiently mining frequent closed partial orders. In: Proceedings of the 5th IEEE International Conference on Data Mining; 2005. p. 753–756.Google Scholar
  10. 10.
    Pei J, Wang H, Liu J, Wang K, Wang J, Yu PS. Discovering frequent closed partial orders from strings. IEEE Trans Knowl Data Eng. 2006;18(11):1467–81.CrossRefGoogle Scholar
  11. 11.
    Wang J, Han J. BIDE: efficient mining of frequent closed sequences. In: Proceedings of the 19th International Conference on Data Engineering; 2003. p. 79–90.Google Scholar
  12. 12.
    Yan X, Han J, Afshar R. CloSpan: mining closed sequential patterns in large datasets. In: Proceedings of the 2003 SIAM International Conference on Data Mining; 2003. p. 166–177.CrossRefGoogle Scholar
  13. 13.
    Zaki M. SPADE: an efficient algorithm for mining frequent sequences. Mach Learn J. 2000;42(1/2):31–60.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Helsinki University of TechnologyHelsinkiFinland

Section editors and affiliations

  • Jian Pei
    • 1
  1. 1.School of Computing ScienceSimon Fraser Univ.BurnabyCanada