Episode Rules Mining Algorithm for Distant Event Prediction

Lina Fahed, Armelle Brun, Anne Boyer

2014

Abstract

This paper focuses on event prediction in an event sequence, where we aim at predicting distant events. We propose an algorithm that mines episode rules, which are minimal and have a consequent temporally distant from the antecedent. As traditional algorithms are not able to mine directly rules with such characteristics, we propose an original way to mine these rules. Our algorithm, which has a complexity similar to that of state of the art algorithms, determines the consequent of an episode rule at an early stage in the mining process, it applies a span constraint on the antecedent and a gap constraint between the antecedent and the consequent. A new confidence measure, the temporal confidence, is proposed, which evaluates the confidence of a rule in relation to the predefined gap. The algorithm is validated on an event sequence of social networks messages. We show that minimal rules with a distant consequent are actually formed and that they can be used to accurately predict distant events.

References

  1. Achar, A., Sastry, P., et al. (2013). Pattern-growth based frequent serial episode discovery. Data & Knowledge Engineering, 87:91-108.
  2. Agrawal, R., ImieliÁski, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. In ACM SIGMOD Record, volume 22, pages 207-216. ACM.
  3. Cho, C.-W., Wu, Y.-H., Yen, S.-J., Zheng, Y., and Chen, A. L. (2011). On-line rule matching for event prediction. The VLDB Journal, 20(3):303-334.
  4. Gan, M. and Dai, H. (2011). Fast mining of non-derivable episode rules in complex sequences. In Modeling Decision for Artificial Intelligence. Springer.
  5. Huang, K.-Y. and Chang, C.-H. (2008). Efficient mining of frequent episodes from complex sequences. Information Systems, 33(1):96-114.
  6. Laxman, S., Sastry, P., and Unnikrishnan, K. (2007). A fast algorithm for finding frequent episodes in event streams. In 13th ACM SIGKDD. ACM.
  7. Laxman, S. and Sastry, P. S. (2006). A survey of temporal data mining. Sadhana, 31(2).
  8. Luo, J. and Bridges, S. M. (2000). Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection. Int. J. of Intelligent Systems, 15(8):687- 703.
  9. Mannila, H., Toivonen, H., and Verkamo, A. I. (1997). Discovery of frequent episodes in event sequences. Data Mining and Knowl. Discovery, 1(3):259-289.
  10. Méger, N. and Rigotti, C. (2004). Constraint-based mining of episode rules and optimal window sizes. In PKDD 2004, pages 313-324. Springer.
  11. Neeraj, S. and Swati, L. S. (2012). Overview of nonredundant association rule mining. Research Journal of Recent Sciences ISSN, 2277:2502.
  12. Ng, A. and Fu, A. W.-C. (2003). Mining frequent episodes for relating financial events and stock trends. In Advances in Knowledge Discovery and Data Mining, pages 27-39. Springer.
  13. Pasquier, N., Bastide, Y., Taouil, R., and Lakhal, L. (1999). Discovering frequent closed itemsets for association rules. In Database Theory-ICDT, pages 398-416. Springer.
  14. Rahal, I., Ren, D., Wu, W., and Perrizo, W. (2004). Mining confident minimal rules with fixed-consequents. In 16th IEEE ICTAI 2004.
Download


Paper Citation


in Harvard Style

Fahed L., Brun A. and Boyer A. (2014). Episode Rules Mining Algorithm for Distant Event Prediction . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 5-13. DOI: 10.5220/0005027600050013


in Bibtex Style

@conference{kdir14,
author={Lina Fahed and Armelle Brun and Anne Boyer},
title={Episode Rules Mining Algorithm for Distant Event Prediction},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={5-13},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005027600050013},
isbn={978-989-758-048-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - Episode Rules Mining Algorithm for Distant Event Prediction
SN - 978-989-758-048-2
AU - Fahed L.
AU - Brun A.
AU - Boyer A.
PY - 2014
SP - 5
EP - 13
DO - 10.5220/0005027600050013