Browse > Article
http://dx.doi.org/10.3745/KIPSTD.2009.16D.5.691

Finding Association Rules based on the Significant Rare Relation of Events with Time Attribute  

Han, Dae-Young (전남대학교 전자컴퓨터공학부)
Kim, Dae-In (전남대학교 전자컴퓨터공학부)
Kim, Jae-In (전남대학교 전자컴퓨터공학부)
Song, Myung-Jin (전남대학교 전자컴퓨터공학부)
Hwang, Bu-Hyun (전남대학교 전자컴퓨터공학부)
Publication Information
The KIPS Transactions:PartD / v.16D, no.5, 2009 , pp. 691-700 More about this Journal
Abstract
An event means a flow which has a time attribute such as the a symptom of patients, an interval event has the time period between the start-time-point and the end-time-point. Although there are many studies for temporal data mining, they do not deal with discovering knowledge from interval event such as patient histories and purchase histories. In this paper, we suggest a method of temporal data mining that finds association rules of event causal relationships and predicts an occurrence of effect event based on discovered rules. Our method can predict the occurrence of an event by summarizing an interval event using the time attribute of an event and finding the causal relationship of event. As a result of simulation, this method can discover better knowledge than others by considering a lot of supports of an event and finding the significant rare relation on interval events which means an essential cause of an event, regardless of an occurrence support of an event in comparison with conventional data mining techniques.
Keywords
Temporal Property; Interval Event; Causal Relationship; Association Rule; Significant Rare Relation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. Lee, J. Lee, D. Chai, B. Hwang, and K. Ryu, "Mining Temporal Interval Relation Rules from Temporal Data," Journal of Systems and Software, Vol.82, pp.155-167, 2009.   DOI   ScienceOn
2 S. Laxman, P. S. Sastry, and K. Unnikrishnan, "Discovering Frequent Episodes and Learning Hidden Markov Models: a Formal Connection," IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.11, pp.1505-1517, Jan., 2006.   DOI   ScienceOn
3 S. Laxman, P. S. Sastry, and K. Unnikrishnan, "Discovering Frequent Generalized Episodes when Events Persist for Different Durations," IEEE Transactions on Knowledge and Data Engineering, Vol.19, No.9, pp.1188-1201, Sep., 2007.   DOI   ScienceOn
4 J. F. Roddick, K. Hornsby, and M. Spiliopoulou, "Temporal, Spatial and Spatio-Temporal Data Mining Knowledge Discovery Research Bibliography," http://www.cs.flinders. edu.au, Sep., 2001.
5 E. Winarko, and J. F. Roddick, "ARMADA - An Algorithm for Discovering Richer Relative Temporal Association Rules from Interval-based Data," Journal of Data and Knowledge Engineering Vol.63, pp.76-90, Oct., 2007.   DOI   ScienceOn
6 H. Zhu, and Z. Xu, "An Effective Algorithm for Mining Positive and Negative Association Rules," Proc. of International Conference on Computer Science and Software Engineering, pp.455-458, Dec. 2008.   DOI
7 H. Yun, D. Ha, B. Hwang, and K. Ryu, "Mining Association Rules on Significant Rare Data Using Relative Support," Journal of Systems and Software, Vol.67, pp.181-191, 2003.   DOI   ScienceOn
8 J. Chung, O. Paek, J. Lee, and K. Ryu, "Temporal Pattern Mining of Moving Objects for Location-Based Service," Proc. of International Conference on Database and Expert Systems Applications Vol.2453, pp.331-340, 2002.   DOI   ScienceOn
9 B. Ozden, S. Ramaswamy, and A. Silberschatz, "Cycle Association Rules," Proc. of International Conference on Data Engineering Orlando, USA, 1998.
10 R. J. Swargam, M. J. Palakal, "The Role of Least Frequent Item Sets in Association Discovery," Proc. of ICDIM '07. 2nd International Conference, Vol.1, pp.217-223, Oct., 2007.   DOI
11 E. Keogh, J. Lin, S. Lee, and H. V. Herle, "Finding the Most Unusual Time Series Subsequences: Algorithms and Applications," Journal of Knowledge and Information Systems Vol.11, No.1, pp.1-27, Dec., 2006.   DOI
12 S. Ramaswamy, S. Mahajan, and A. Silberschatz, "On the Discovery of Interesting Patterns in Association Rules," Proc. of 24th VLDB Conference New York, USA, pp.368-379, 1998.