Definition
Given a set D of n partial orders on S, and a threshold σ ≤ n, 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...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Agrawal R. and Srikant R. Mining sequential patterns. In Proc. 11th Int. Conf. on Data Engineering, 1995, pp. 3–14.
Ben-Dor A., Chor B., Karp R., and Yakhini Z. Discovering Local Structure in Gene-Expression Data: The Order Preserving Submatrix Problem. In Proc. 6th Annual Int. Conf. on Computational Biology, 2002, pp. 49–57.
Fernandez P.L., Heath L.S., Ramakrishnan N., and Vergara J.P. Reconstructing Partial Orders from Linear Extensions. In Proc. 4th SIGKDD Workshop on Temporal Data Mining: Network Reconstruction from Dynamic Data, 2006.
Gwadera R., Atallah M.J., and Szpankowski W. Reliable Detection of Episodes in Event Sequences. In Proc. 2003 IEEE Int. Conf. on Data Mining, 2003, pp. 67–74.
Laxman S., Sastry P.S., and Unnikrishnan K.P. A fast algorithm for finding frequent episodes in event streams. In Proc. 13th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2007, pp. 410–419.
Mannila H. and Meek C. Global Partial Orders from Sequential Data. In Proc. 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2000, pp. 161–168.
Mannila H., Toivonen H., and Verkamo I. Discovering frequent episodes in sequences. In Proc. 1st Int. Conf. on Knowledge Discovery and Data Mining, 1995, pp. 210–215.
Pei J., Han J., Mortazavi-Asl B., Pinto H., Chen Q., Dayal U., and Hsu M.-C. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In Proc. 17th Int. Conf. on Data Engineering, 2001, pp. 215–224.
Pei J., Liu J., Wang H., Wang K., Yu P.S., and Wang J. Efficiently Mining Frequent Closed Partial Orders. In Proc. 2005 IEEE Int. Conf. on Data Mining, 2005, pp. 753–756.
Pei J., Wang H., Liu J., Wang K., Wang J., and Yu P.S. Discovering frequent closed partial orders from strings. IEEE Trans. Knowl. Data Eng., 18(11):1467–1481, 2006.
Wang J. and Han J. BIDE: Efficient Mining of Frequent Closed Sequences. In Proc. 19th Int. Conf. on Data Engineering, 2003, pp. 79–90.
Yan X., Han J., and Afshar R. CloSpan: Mining Closed Sequential Patterns in Large Datasets. In Proc. SIAM International Conference on Data Mining, 2003, pp. 166–177.
Zaki M. SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. J., 42(1/2):31–60, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this entry
Cite this entry
Ukkonen, A. (2009). Frequent Partial Orders. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_172
Download citation
DOI: https://doi.org/10.1007/978-0-387-39940-9_172
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-35544-3
Online ISBN: 978-0-387-39940-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering