Frequent and Significant Episodes in Sequences of Events - Computation of a New Frequency Measure based on Individual Occurrences of the Events

Oscar Quiroga, Joaquim Meléndez, Sergio Herraiz

2012

Abstract

Pattern discovery in event sequences is based on the mining of frequent episodes. Patterns are the result of the assessment of frequent episodes using episode rules. However, with a simple search usually a huge number of frequent episodes and rules are found, then, methods to recognise the most significant patterns and to properly measure the frequency of the episodes, are required. In this paper, two new indexes called cohesion and backward-confidence of the episodes are proposed to help in the extraction of significant patterns. Also, two methods to find the maximal number of non-redundant occurrences of serial and parallel episodes are presented. Experimental results demonstrate the compactness of the mining result and the efficiency of our mining algorithms.

References

  1. Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules. In Int. Conf. Very Large Data Bases (VLDB'94).
  2. Agrawal, R. and Srikant, R. (1995). Mining sequential patterns. In Int. Conf. Data Engineering (ICDE'95), pages 3-14.
  3. Casas-Garriga, G. (2003). Discovering unbounded episodes in sequential data. In Lavrac, N., Gamberger, D., Todorovski, L., and Blockeel, H., editors, Knowledge Discovery in Databases: PKDD 2003, volume 2838 of Lecture Notes in Computer Science, pages 83-94. Springer Berlin / Heidelberg.
  4. Doucet, A. and Ahonen-Myka, H. (2006). Fast extraction of discontiguous sequences in text: a new approach based on maximal frequent sequences. Proceedings of IS-LTC, 2006:186-191.
  5. Gan, M. and Dai, H. (2010). A study on the accuracy of frequency measures and its impact on knowledge discovery in single sequences. In Data Mining Workshops (ICDMW), 2010 IEEE International Conference on, pages 859-866.
  6. Gan, M. and Dai, H. (2011). Fast mining of non-derivable episode rules in complex sequences. In Torra, V., Narakawa, Y., Yin, J., and Long, J., editors, Modeling Decision for Artificial Intelligence, volume 6820 of Lecture Notes in Computer Science, pages 67-78. Springer Berlin / Heidelberg.
  7. Iwanuma, K., Ishihara, R., Takano, Y., and Nabeshima, H. (2005). Extracting frequent subsequences from a single long data sequence a novel anti-monotonic measure and a simple on-line algorithm. In Data Mining, Fifth IEEE International Conference on, page 8 pp.
  8. Laxman, S., Sastry, P., and Unnikrishnan, K. (2007). Discovering frequent generalized episodes when events persist for different durations. Knowledge and Data Engineering, IEEE Transactions on, 19(9):1188- 1201.
  9. Laxman, S., Sastry, P. S., and Unnikrishnan, K. P. (2004). Fast algorithms for frequent episode discovery in event sequences. Technical report, CL-2004-04/MSR, GM R&D Center, Warren.
  10. Mannila, H., Toivonen, H., and Verkamo, A. I. (1997). Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1(3):259-289.
  11. Patnaik, D. (2006). Application of frequent episode framework in microelectrode array data analysis. Master's thesis, Dept. Electrical Engineering, Indian Institute of Science, Bangalore.
  12. Zhou, W., Liu, H., and Cheng, H. (2010). Mining closed episodes from event sequences efficiently. In Proceedings of the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining(1), pages 310-318.
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Paper Citation


in Harvard Style

Quiroga O., Meléndez J. and Herraiz S. (2012). Frequent and Significant Episodes in Sequences of Events - Computation of a New Frequency Measure based on Individual Occurrences of the Events . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 324-328. DOI: 10.5220/0004118003240328


in Bibtex Style

@conference{kdir12,
author={Oscar Quiroga and Joaquim Meléndez and Sergio Herraiz},
title={Frequent and Significant Episodes in Sequences of Events - Computation of a New Frequency Measure based on Individual Occurrences of the Events},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={324-328},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004118003240328},
isbn={978-989-8565-29-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - Frequent and Significant Episodes in Sequences of Events - Computation of a New Frequency Measure based on Individual Occurrences of the Events
SN - 978-989-8565-29-7
AU - Quiroga O.
AU - Meléndez J.
AU - Herraiz S.
PY - 2012
SP - 324
EP - 328
DO - 10.5220/0004118003240328