Abstract
Finding the longest sequential pattern is a basic task in data mining. Current algorithm often depends on the cascading support counting, including the famous apriori algorithm, FP-growth, and some likewise derived algorithms. One thing should be pointed out that in these sorts of algorithms the items with very high support may lead to a poor time performance and very huge useless search space, especially when the items in fact are not the member of the result longest pattern. We reconsider the role of connection between the items and carefully analysis the hidden chains connecting items. Thus a new method of mining the longest pattern in transaction database is proposed in this paper. Our algorithm can have better performance when overcoming the bad side-effect of big-support items, especially in case of the items being not members of the result longest pattern.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14. IEEE Computer Society Press (1995)
Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)
Masseglia, F., Cathala, F., Poncelet, P.: The PSP Approach for Mining Sequential Patterns. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 176–184. Springer, Heidelberg (1998)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H.: Prefixspan:Mining Sequential Patterns Efficiently by Prefix-projected Pattern Growth. In: Proc. of the 17th International Conf. on Data Engineering, ICDE 2001, pp. 215–226 (2001)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns without Candidate Generation. In: Proc. 2000 ACM-SiGMOD Int’l Conf. Management of Data, SIGMOD 2000, pp. 1–12 (2000)
Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q.: FreeSpan: FrequentPattern-Projected Sequential Pattern Mining. In: Proc. 2000 Int. Conf. Knowledge Discovery and Data Mining, KDD 2000, pp. 355–359. Boston, MA (2000)
Zaki, M.J.: Spade: An Efficient Algorithm for Mining Frequents Sequences. Machine Learning 42, 31–60 (2001)
Ayres, J., Gehrke, J., Yiu, T., Flannick, J.: Sequential PAttern Mining using A Bitmap Representation. In: SIGKDD 2001, Edmonton, Alberta, Canada (2001)
Zhu, F., Yan, X., Han, J., Yu, P.S.: Mining colossal frequent patterns by core pattern fusion. In: Proc. 2007 Int. Conf. Data Engineering, ICDE 2007 (2007)
Rabiner, L.R.: Proceedings of the IEEE, vol. 77(2) (February 1989)
Panuccio, A., Bicego, M., Murino, V.: A hidden markov model-based approach to sequential data clustering. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, p. 734. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Huang, Q., Ouyang, W. (2014). Mining Longest Frequent Patterns with Low-Support Items. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_57
Download citation
DOI: https://doi.org/10.1007/978-3-319-09339-0_57
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09338-3
Online ISBN: 978-3-319-09339-0
eBook Packages: Computer ScienceComputer Science (R0)