Abstract
In this paper, first we introduce a bipartite episode of the form A ↦B for two sets A and B of events, which means that every event of A is followed by every event of B. Then, we present an algorithm that finds all frequent bipartite episodes from an input sequence without duplication in O(|Σ| ·N) time per an episode and in O(|Σ|2 n) space, where Σ is an alphabet, N is total input size of \(\mathcal S\), and n is the length of S. Finally, we give experimental results on artificial and real sequences to evaluate the efficiency of the algorithm.
This work is partially supported by Grand-in-Aid for JSPS Fellows (20·3406).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. 20th VLDB, pp. 487–499 (1994)
Arimura, H.: Efficient algorithms for mining frequent and closed patterns from semi-structured data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 2–13. Springer, Heidelberg (2008)
Arimura, H., Uno, T.: A polynomial space and polynomial delay algorithm for enumeration of maximal motifs in a sequence. In: Deng, X., Du, D.-Z. (eds.) ISAAC 2005. LNCS, vol. 3827, pp. 724–737. Springer, Heidelberg (2005)
Avis, D., Fukuda, K.: Reverse search for enumeration. Discrete Applied Mathematics 65, 21–46 (1996)
Katoh, T., Hirata, K.: Mining frequent elliptic episodes from event sequences. In: Proc. 5th LLLL, pp. 46–52 (2007)
Katoh, T., Hirata, K.: A simple characterization on serially constructible episodes. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 600–607. Springer, Heidelberg (2008)
Katoh, T., Arimura, H., Hirata, K.: A Polynomial-Delay Polynomial-Space Algorithm for Extracting Frequent Diamond Episodes from Event Sequences. In: Theeramunkong, T., et al. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 172–183. Springer, Heidelberg (2009)
Katoh, T., Hirata, K., Harao, M.: Mining sectorial episodes from event sequences. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds.) DS 2006. LNCS (LNAI), vol. 4265, pp. 137–148. Springer, Heidelberg (2006)
Katoh, T., Hirata, K., Harao, M.: Mining frequent diamond episodes from event sequences. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds.) MDAI 2007. LNCS (LNAI), vol. 4617, pp. 477–488. Springer, Heidelberg (2007)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1, 259–289 (1997)
Pei, J., Wang, H., Liu, J., Wang, K., Wang, J., Yu, P.S.: Discovering frequent closed partial orders from strings. IEEE TKDE 18, 1467–1481 (2006)
Pei, J., Han, J., Mortazavi-Asi, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: Mining sequential patterns by pattern-growth: The PrefixSpan approach. IEEE Trans. Knowledge and Data Engineering. 16, 1–17 (2004)
Uno, T.: Two general methods to reduce delay and change of enumeration algorithms, NII Technical Report, NII-2003-004E (April 2003)
Zaki, M.J.: Scalable Algorithms for Association Mining. IEEE TKDE 12, 372–390 (2000)
Zaki, M.J., Hsiao, C.-J.: CHARM: An efficient algorithm for closed itemset mining. In: Proc. 2nd SDM, pp. 457–478. SIAM, Philadelphia (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Katoh, T., Arimura, H., Hirata, K. (2009). Mining Frequent Bipartite Episode from Event Sequences. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds) Discovery Science. DS 2009. Lecture Notes in Computer Science(), vol 5808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04747-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-04747-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04746-6
Online ISBN: 978-3-642-04747-3
eBook Packages: Computer ScienceComputer Science (R0)