Abstract
The increased use of data mining algorithms reflects the need for automatic extraction of knowledge from large volumes of data. This work presents a temporal data mining algorithm that discovers frequent Association Rules from timestamped data. These rules are named Cause-Effect Rules, each represented by a multiset of unordered events (Cause) followed by a singleton event (Effect). Also, a Cause-Effect Rule is valid within an specific constraint that defines the minimum and maximum time distance between its Cause and Effect. Our algorithm was tested on a data set from two hospital emergency departments in Sherbrooke, QC, Canada.
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
Similar content being viewed by others
Notes
- 1.
The multiset is a flexible representation that permits the Cause to contain repeated events.
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. Proc. VLDB 1215, 487–499 (1994)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th ICDE, pp. 3–14. IEEE (1995)
Cule, B., Tatti, N., Goethals, B.: Marbles: mining association rules buried in long event sequences. Stat. Anal. DM: ASA Data Sci. J. 7(2), 93–110 (2014)
Gomes, H.M., Carvalho, D.R.: A hybrid data mining method: exploring sequential indicators over association rules. Iberoamerican J. Appl. Comput. 1(1), 40–60 (2011)
Lucas, J., Batt, R.J., Soremekun, O.A.: Setting wait times to achieve targeted left-without-being-seen rates. Am. J. Emerg. Med. 32(4), 342–345 (2014)
Mannila, H., Toivonen, H., Inkeri Verkamo, A.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov. 1(3), 259–289 (1997)
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. 1–17. Springer, Heidelberg (1996)
Zaki, M.J.: Spade: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1–2), 31–60 (2001)
Zubieta, L., Fernández-Peña, J.R.: A retrospective study on emergency visits in two hospitals in sherbrooke, Canada. J. Hosp. Adm. 3(4), p61 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Gomes, H.M., de Carvalho, D.R., Zubieta, L., Barddal, J.P., Malucelli, A. (2015). On the Discovery of Time Distance Constrained Temporal Association Rules. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_58
Download citation
DOI: https://doi.org/10.1007/978-3-319-26535-3_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26534-6
Online ISBN: 978-3-319-26535-3
eBook Packages: Computer ScienceComputer Science (R0)