Abstract
Many of recent computer applications generate data as a form of data streams, so a study on mining data streams can give valuable results being widely used in the applications. In this paper, a novel weighting technique for mining interesting sequential patterns over a sequence data stream is proposed. Assuming that a sequence with small time-intervals between its data elements is more valuable than others with large time-intervals, the novel interesting sequential pattern is defined and found by analyzing the time-intervals of data elements in a sequence as well as their orders.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lo S (2005) Binary prediction based on weighted sequential mining method. In: Proceedings of the 2005 international conference on web intelligence, 755–761
Yun U (2007) WIS: Weighted interesting sequential pattern mining with a similar level of support and/or weight. ETRI J 29:336–352
Yun U (2008) A new framework for detecting weighted sequential patterns in large sequence databases. Knowl-Based Syst 21:110–122
Chen Y-L, Chiang, M-C, Ko M-T (2005) Discovering fuzzy time-interval sequential patterns in sequence databases. IEEE Trans Syst Man Cybern-Part B: Cybern 35:959–972
Chen Y-L, Huang TC-H (2003) Discovering time-interval sequential patterns in sequence databases. Expert Syst Appl 25:343–354
Chang JH, Lee WS (2005) Efficient mining method for retrieving sequential patterns over online data streams. J Inf Sci 31:420–432
Ji X, Bailey J, Dong G (2007) Mining minimal distinguishing subsequence patterns with gap constraints. Knowl Inf Syst 11:259–296
Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the 1995 international conference on data engineering, 3–14
Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2012R1A1B4000651)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Chang, J.H., Park, NH. (2013). A Novel Weighting Technique for Mining Sequence Data Streams. In: Kim, K., Chung, KY. (eds) IT Convergence and Security 2012. Lecture Notes in Electrical Engineering, vol 215. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5860-5_112
Download citation
DOI: https://doi.org/10.1007/978-94-007-5860-5_112
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5859-9
Online ISBN: 978-94-007-5860-5
eBook Packages: EngineeringEngineering (R0)