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Evaluation Measures for Extended Association Rules Based on Distributed Representations

  • Tomonobu OzakiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)

Abstract

Indirect association rules and association action rules are two notable extensions of traditional association rules. Since these two extended rules consist of a pair of association rules, they share the same essential drawback of association rules: a huge number of rules will be derived if the target database to be mined is dense or the minimum threshold is set low. One practical approach for alleviating this essential drawback is to rank the rules to identify which one to be examined first in a post-processing. In this paper, as a new application of representation learning, we propose evaluation measures for indirect association rules and association action rules, respectively. The proposed measures are assessed preliminary using a dataset on Japanese video-sharing site and that on nursery.

Notes

Acknowledgements

In this paper, the author used the “Nicovideo dataset” provided by National Institute of Informatics. This work was partially supported by JSPS KAKENHI Grant Number 17K00315.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM-SIGMOD International Conference on Management of Data, pp. 207–216 (1993)Google Scholar
  2. 2.
    Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Disc. 15(1), 55–86 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Boulicaut, J.-F., Bykowski, A., Rigotti, C.: Free-sets: a condensed representation of boolean data for the approximation of frequency queries. Data Min. Knowl. Disc. 7(1), 5–22 (2003)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Proceedings of the 7th International Conference on Database Theory, pp. 398–416 (1999)Google Scholar
  5. 5.
    Webb, G.I.: Discovering significant patterns. Mach. Learn. 68(1), 1–33 (2007)CrossRefGoogle Scholar
  6. 6.
    Hämäläinen, W., Webb, G.I.: Statistically sound pattern discovery. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 1976 (2014)Google Scholar
  7. 7.
    Wu, T., Chen, Y., Han, J.: Re-examination of interestingness measures in pattern mining: a unified framework. Data Min. Knowl. Disc. 21(3), 371–397 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Lenca, P., Vaillant, B., Meyer, P., Lallich, S.: Association rule interestingness measures: experimental and theoretical studies. In: Guillet, F.J., Hamilton, H.J. (eds.) Quality Measures in Data Mining, pp. 51–76 (2007)Google Scholar
  9. 9.
    Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: generalizing association rules to correlations. In: Proceedings of the 1997 ACM SIGMOD/PODS Joint Conference, pp. 265–276 (1997)Google Scholar
  10. 10.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, pp. 255–264 (1997)Google Scholar
  11. 11.
    Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)CrossRefGoogle Scholar
  12. 12.
    Kawanobe, S., Ozaki, T.: Extraction of characteristic frequent visual patterns by distributed representation. In: Proceedings of the 2017 31st International Conference on Advanced Information Networking and Applications Workshops, pp. 525–530 (2017)Google Scholar
  13. 13.
    Kawanobe, S., Ozaki, T.: Experimental study of characterizing frequent itemsets using representation learning. In: Proceedings of the 2018 32nd International Conference on Advanced Information Networking and Applications Workshops, pp. 170–174 (2018)Google Scholar
  14. 14.
    Ozaki, T.: Evaluation measures for frequent itemsets based on distributed representations. In: Proceedings of the 2018 Sixth International Symposium on Computing and Networking, pp. 153–159 (2018)Google Scholar
  15. 15.
    Tan, P.-N., Kumar, V., Srivastava, J.: Indirect association: mining higher order dependencies in data. In: Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 632–637 (2000)Google Scholar
  16. 16.
    Ras, Z.W., Dardzinska, A., Tsay, L.-S., Wasyluk, H.: Association action rules. In: Proceedings of the 2018 IEEE International Conference on Data Mining Workshop, pp. 283–290 (2008)Google Scholar
  17. 17.
    Kawaguchi, M., Ozaki, T.: Finding replaceable ingredients by indirect association rules. In: Proceedings of the 78th National Convention of Information Processing Society of Japan, vol. 1, pp. 525–526 (2016). (in Japanese)Google Scholar
  18. 18.
    Suzuki, E.: Discovering action rules that are highly achievable from massive data. In: Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp. 713–722 (2009)Google Scholar
  19. 19.
    Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)Google Scholar
  20. 20.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint, arXiv:1301.3781 (2013)
  21. 21.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119 (2013)Google Scholar
  22. 22.
    Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)
  23. 23.
    Arora, S., Liang, Y., Ma, T.: A simple but tough-to-beat baseline for sentence embeddings. In: Proceedings of 5th International Conference on Learning Representations (2017)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Humanities and SciencesNihon UniversityTokyoJapan

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