MARBLES: Mining association rules buried in long event sequences

Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012

Volumn , Issue , 2012, Pages 248-259

  Cule, Boris ^a   Tatti, Nikolaj ^a   Goethals, Bart ^a  

^a UNIVERSITY OF ANTWERP   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION RULES;

CONFIDENCE MEASURE; EVENT SEQUENCE; FREQUENT EPISODES; MINING ASSOCIATIONS; NON-REDUNDANT; PRUNING STEPS; REDUNDANT RULES; SEQUENTIAL PATTERNS;

DATA MINING;

EID: 84880214637     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972825.22     Document Type: Conference Paper

References (24)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB 1994), pages 487-499, 1994.
2. R. Agrawal and R. Srikant. Mining sequential patterns. 11th International Conference on Data Engineering (ICDE 1995), 0:3-14, 1995.
3. Y. Bastide, N. Pasquier, R. Taouil, G. Stumme, and L. Lakhal. Mining minimal non-redundant association rules using frequent closed itemsets. In Computational Logic, pages 972-986, 2000.
4. T. Calders, N. Dexters, and B. Goethals. Mining frequent itemsets in a stream. In Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), pages 83-92, 2007.
5. G. Casas-Garriga. Discovering unbounded episodes in sequential data. In Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 83-94, 2003.
6. G. Casas-Garriga. Summarizing sequential data with closed partial orders. In Proceedings of the SIAM International Conference on Data Mining (SDM 2005), pages 380-391, 2005.
7. B. Cule and B. Goethals. Mining association rules in long sequences. In Proceedings of the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2010), pages 300-309, 2010.
8. B. Cule, B. Goethals, and C. Robardet. A new constraint for mining sets in sequences. In Proceedings of the SIAM International Conference on Data Mining (SDM 2009), pages 317-328, 2009.
9. M. Garofalakis, R. Rastogi, and K. Shim. Mining se-quential patterns with regular expression constraints. IEEE Transactions on Knowledge and Data Engineer- ing, 14(3):530-552, 2002.
10. R. Gwadera, M. J. Atallah, and W. Szpankowski. Markov models for identification of significant episodes. In Proceedings of the SIAM International Conference on Data Mining (SDM 2005), pages 404-414, 2005.
11. R. Gwadera, M. J. Atallah, and W. Szpankowski. Re- liable detection of episodes in event sequences. Knowl- edge and Information Systems, 7(4):415-437, 2005.
12. S. K. Harms, J. S. Deogun, J. Saquer, and T. Tadesse. Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints. In Proceedings of the IEEE In- Ternational Conference on Data Mining (ICDM 2001), pages 603-606, 2001.
13. S. Laxman, P. S. Sastry, and K. P. Unnikrishnan. A fast algorithm for finding frequent episodes in event streams. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge discovery and data mining (KDD 2007), pages 410-419, 2007.
14. H. Mannila, H. Toivonen, and A. I. Verkamo. Dis- covery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1(3):259-289, 1997.
15. N. Meger and C. Rigotti. Constraint-based mining of episode rules and optimal window sizes. In Knowledge Discovery in Databases: PKDD 2004, 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 313-324, 2004.
16. J. Pei, H. Wang, J. Liu, K. Wang, J. Wang, and P. S. Yu. Discovering frequent closed partial orders from strings. IEEE Transactions on Knowledge and Data Engineering, 18(11):1467-1481, 2006.
17. N. Tatti. Significance of episodes based on minimal windows. In Proceedings of the 9th IEEE International Conference on Data Mining (ICDM 2009), pages 513- 522, 2009.
18. N. Tatti and B. Cule. Mining closed episodes with si- multaneous events. In Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2011), pages 1172-1180, 2011.
19. N. Tatti and B. Cule. Mining closed strict episodes. Data Mining and Knowledge Discovery, 2011. http://dx.doi.org/10.1007/s10618-011-0232-z.
20. P. Tzvetkov, X. Yan, and J. Han. Tsp: Mining top- k closed sequential patterns. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), pages 347-354, 2003.
21. J. Wang and J. Han. Bide: Efficient mining of frequent closed sequences. 20th International Conference on Data Engineering (ICDE 2004), 0:79, 2004.
22. J. T.-L. Wang, G.-W. Chirn, T. G. Marr, B. Shapiro, D. Shasha, and K. Zhang. Combinatorial pattern discovery for scientific data: some preliminary results. ACM SIGMOD Record, 23(2):115-125, 1994.
23. X. Yan, J. Han, and R. Afshar. Clospan: Mining closed sequential patterns in large datasets. In Proceedings of the SIAM International Conference on Data Mining (SDM 2003), pages 166-177, 2003.
24. W. Zhou, H. Liu, and H. Cheng. Mining closed episodes from event sequences efficiently. In Proceed- ings of the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining(1), pages 310-318, 2010.