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KAPMiner: Mining Ordered Association Rules with Constraints

  • Isak Karlsson
  • Panagiotis Papapetrou
  • Lars Asker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10584)

Abstract

We study the problem of mining ordered association rules from event sequences. Ordered association rules differ from regular association rules in that the events occurring in the antecedent (left hand side) of the rule are temporally constrained to occur strictly before the events in the consequent (right hand side). We argue that such constraints can provide more meaningful rules in particular application domains, such as health care. The importance and interestingness of the extracted rules are quantified by adapting existing rule mining metrics. Our experimental evaluation on real data sets demonstrates the descriptive power of ordered association rules against ordinary association rules.

Notes

Acknowledgments

This work was partly supported by grants provided by the Stockholm County Council (SU-SLL). The work of Panagiotis Papapetrou was also partly supported by the VR-2016-03372 Swedish Research Council Starting Grant.

References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of VLDB, pp. 487–499 (1994)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of IEEE ICDE, pp. 3–14 (1995)Google Scholar
  3. 3.
    Ayres, J., Gehrke, J., Yiu, T., Flannick, J.: Sequential pattern mining using a bitmap representation. In: Proceedings of ACM SIGKDD, pp. 429–435 (2002)Google Scholar
  4. 4.
    Bayardo, R.J.: Efficiently mining long patterns from databases. In: Proceedings of ACM SIGMOD, pp. 85–93 (1998)Google Scholar
  5. 5.
    Fournier-Viger, P., Faghihi, U., Nkambou, R., Nguifo, E.M.: Cmrules: mining sequential rules common to several sequences. Know.-Based Syst. 25(1), 63–76 (2012). http://dx.doi.org/10.1016/j.knosys.2011.07.005
  6. 6.
    Fournier-Viger, P., Gueniche, T., Zida, S., Tseng, V.S.: ERMiner: sequential rule mining using equivalence classes. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds.) IDA 2014. LNCS, vol. 8819, pp. 108–119. Springer, Cham (2014). doi: 10.1007/978-3-319-12571-8_10 Google Scholar
  7. 7.
    Fournier-Viger, P., Nkambou, R., Tseng, V.S.M.: Rulegrowth: mining sequential rules common to several sequences by pattern-growth. In: Proceedings of the 2011 ACM Symposium on Applied Computing, SAC 2011, NY, USA, pp. 956–961 (2011). http://doi.acm.org/10.1145/1982185.1982394
  8. 8.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Kamber, M., Shinghal, R.: Evaluating the interestingness of characteristic rules. In: Proceedings of ACM SIGKDD, pp. 263–266 (1996)Google Scholar
  10. 10.
    Laxman, S., Sastry, P.S., Unnikrishnan, K.P.: 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), San Jose, USA, pp. 410–419. Association for Computing Machinery, Inc., August 2007. https://www.microsoft.com/en-us/research/publication/a-fast-algorithm-for-finding-frequent-episodes-in-event-streams/
  11. 11.
    Leleu, M., Rigotti, C., Boulicaut, J., Euvrard, G.: Go-spade: mining sequential patterns over databases with consecutive repetitions. In: Proceedings of MLDM, pp. 293–306 (2003)Google Scholar
  12. 12.
    Mannila, H., Toivonen, H., Verkamo, A.: Discovering frequent episodes in sequences. In: Proceedings of ACM SIGKDD, pp. 210–215 (1995)Google Scholar
  13. 13.
    Omiecinski, E.R.: Alternative interest measures for mining associations in databases. IEEE Trans. Knowl. Data Eng. 15(1), 39–79 (2003)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Pei, J., Han, J., Mao, R.: Closet: an efficient algorithm for mining frequent closed itemsets. In: Proceedings of DMKD, pp. 11–20 (2000)Google Scholar
  15. 15.
    Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of IEEE ICDE, pp. 215–224 (2001)Google Scholar
  16. 16.
    Petitjean, F., Li, T., Tatti, N., Webb, G.I.: Skopus: mining top-k sequential patterns under leverage. Data Min. Knowl. Discov. 30(5), 1086–1111 (2016). http://dx.doi.org/10.1007/s10618-016-0467-9
  17. 17.
    Socialstyrelsen: Nationella riktlinjer för hjärtsjukvård (2015). http://www.socialstyrelsen.se
  18. 18.
    Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1–17. Springer, Heidelberg (1996). doi: 10.1007/BFb0014140 CrossRefGoogle Scholar
  19. 19.
    Tan, P., Kumar, V.: Interestingness measures for association patterns: a perspective. Tech. Rep. TR00-036, Department of Computer Science, University of Minnesota (2000)Google Scholar
  20. 20.
    Tan, P., Kumar, V., Srivastava, J.: Proceedings of ACM SIGKDD, pp. 183–192, July 2002Google Scholar
  21. 21.
    Wang, J., Han, J.: Bide: efficient mining of frequent closed sequences. In: Proceedings of IEEE ICDE, pp. 79–90 (2004)Google Scholar
  22. 22.
    Webb, G.I.: Discovering significant rules. In: Proceedings of ACM SIGKDD (2006)Google Scholar
  23. 23.
    Webb, G.I., Zhang, S.: K-optimal rule discovery. Data Min. Knowl. Discov. 10(1), 39–79 (2005)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Xin, D., Shen, X., Mei, Q., Han, J.: Discovering interesting patterns through user’s interactive feedback. In: Proceedings of ACM SIGKDD (2006)Google Scholar
  25. 25.
    Yan, X., Han, J., Afshar, R.: Clospan: mining closed sequential patterns in large databases. In: Proceedings of SDM (2003)Google Scholar
  26. 26.
    Zaki, M.: Spade: an efficient algorithm for mining frequent sequences. Mach. Learn. 40, 31–60 (2001)CrossRefzbMATHGoogle Scholar
  27. 27.
    Zaki, M., Hsiao, C.: Charm: an efficient algorithm for closed itemset mining. In: Proceedings of SIAM, pp. 457–473 (2002)Google Scholar
  28. 28.
    Zhang, A., Shi, W., Webb, G.I.: Mining significant association rules from uncertain data. Data Min. Knowl. Discov. 30(4), 928–963 (2016)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Isak Karlsson
    • 1
  • Panagiotis Papapetrou
    • 1
  • Lars Asker
    • 1
  1. 1.Department of Computer and Systems SciencesStockholm UniversityStockholmSweden

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