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Representative Rule Templates for Association Rules Satisfying Multiple Canonical Evaluation Criteria

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Intelligent Information and Database Systems (ACIIDS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10751))

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Abstract

Originally, strong association rules were defined as those that have sufficiently high values of two parameters: support and confidence. However, it has been shown in the literature that in general neither of these two measures is capable of determining (in)dependence between rules’ constituents correctly. Thus, usage of other measures for rule evaluation is also under research. In this paper, we formulate a generic notion of a canonical measure and show important examples of canonical measures. For association rules satisfying any set of criteria based on canonical measures, we offer their concise lossless representation in the form of so called representative rule templates. Also, we derive a number of properties of this representation.

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References

  1. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)

    Google Scholar 

  2. Bastide, Y., Pasquier, N., Taouil, R., Stumme, G., Lakhal, L.: Mining minimal non-redundant association rules using frequent closed itemsets. In: Lloyd, J., et al. (eds.) CL 2000. LNCS (LNAI), vol. 1861, pp. 972–986. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44957-4_65

    Chapter  Google Scholar 

  3. Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: ACM SIGMOD 1997 International Conference on Management of Data, pp. 255–264 (1997)

    Google Scholar 

  4. Hamrouni, T., Yahia, S.B., Nguifo, E.M.: Succinct minimal generators: theoretical foundations and applications. Int. J. Found. Comput. Sci. 19(2), 271–296 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  5. Hilderman, R.J., Hamilton, H.J.: Evaluation of interestingness measures for ranking discovered knowledge. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 247–259. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45357-1_28

    Chapter  Google Scholar 

  6. Kryszkiewicz, M.: Closed set based discovery of representative association rules. In: Hoffmann, F., Hand, D.J., Adams, N., Fisher, D., Guimaraes, G. (eds.) IDA 2001. LNCS, vol. 2189, pp. 350–359. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44816-0_35

    Chapter  Google Scholar 

  7. Kryszkiewicz, M.: Concise representations of frequent patterns and association rules. Prace Naukowe Politechniki Warszawskiej. Elektronika 142, 5–207 (2002)

    MATH  Google Scholar 

  8. Kryszkiewicz, M.: Concise representations of association rules. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds.) Pattern Detection and Discovery. LNCS (LNAI), vol. 2447, pp. 92–109. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45728-3_8

    Chapter  Google Scholar 

  9. Kryszkiewicz, M.: Dependence factor for association rules. In: Nguyen, N.T., Trawiński, B., Kosala, R. (eds.) ACIIDS 2015. LNCS (LNAI), vol. 9012, pp. 135–145. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15705-4_14

    Google Scholar 

  10. Kryszkiewicz, M.: Dependence factor as a rule evaluation measure. In: Matwin, S., Mielniczuk, J. (eds.) Challenges in Computational Statistics and Data Mining. SCI, vol. 605, pp. 205–223. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-18781-5_12

    Chapter  Google Scholar 

  11. Kryszkiewicz, M.: A lossless representation for association rules satisfying multiple evaluation criteria. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.-P. (eds.) ACIIDS 2016. LNCS (LNAI), vol. 9622, pp. 147–158. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49390-8_14

    Chapter  Google Scholar 

  12. Kryszkiewicz, M.: ACBC-adequate association and decision rules versus key generators and rough sets approximations. Fundam. Inform. 148(1–2), 65–85 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  13. Lavrač, N., Flach, P., Zupan, B.: Rule evaluation measures: a unifying view. In: Džeroski, S., Flach, P. (eds.) ILP 1999. LNCS (LNAI), vol. 1634, pp. 174–185. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48751-4_17

    Chapter  Google Scholar 

  14. Lenca, P., Meyer, P., Vaillant, B., Lallich, S.: On selecting interestingness measures for association rules: user oriented description and multiple criteria decision aid. Eur. J. Oper. Res. 184, 610–626 (2008). Elsevier

    Article  MATH  Google Scholar 

  15. Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, pp. 229–248, AAAI/MIT Press (1991)

    Google Scholar 

  16. Sheikh, L.M., Tanveer, B., Hamdani, S.M.A.: Interesting measures for mining association rules. In: Proceedings of INMIC 2004. IEEE (2004)

    Google Scholar 

  17. Shortliffe, E., Buchanan, B.: A model of inexact reasoning in medicine. Math. Biosci. 23, 351–379 (1975)

    Article  MathSciNet  Google Scholar 

  18. Suzuki, E.: Interestingness measures - limits, desiderata, and recent results. In: Lenca, P., Lallich, S. (eds.) QIMIE/PAKDD 2009 (2009)

    Google Scholar 

  19. Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Intelligent structuring and reducing of association rules with formal concept analysis. In: Baader, F., Brewka, G., Eiter, T. (eds.) KI 2001. LNCS (LNAI), vol. 2174, pp. 335–350. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45422-5_24

    Chapter  Google Scholar 

  20. Zaki, M.J.: Generating non-redundant association rules. In: 6th ACM SIGKDD (2000)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Marzena Kryszkiewicz

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  1. Marzena Kryszkiewicz
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Correspondence to Marzena Kryszkiewicz .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Quang Binh University, Dong Hoi City, Vietnam

    Duong Hung Hoang

  3. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  4. Rutgers University, Piscataway, New Jersey, USA

    Hoang Pham

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Kryszkiewicz, M. (2018). Representative Rule Templates for Association Rules Satisfying Multiple Canonical Evaluation Criteria. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10751. Springer, Cham. https://doi.org/10.1007/978-3-319-75417-8_52

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  • DOI: https://doi.org/10.1007/978-3-319-75417-8_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75416-1

  • Online ISBN: 978-3-319-75417-8

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