Abstract
While a large number of objective interestingness measures have been proposed to extract interesting rules from a dataset, most of them have been tested on a limited number of datasets that may not cover all possible patterns. This paper presents a framework to investigate relation among twenty-one interestingness measures on synthesized patterns (A → B), using all combinations of the six probabilities P(A, B), P(A, ¬B), P(¬A, B), P(¬A, ¬B), P(A) and, P(B) with a fixed number of occurrences. The partial order of interestingness measures is compared to that of another measure in order to characterize their similarity. The result shows 75 interrelation patterns of probabilities. An association rule mining is used to analyzed to describe for understanding their common and distinct properties.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD International Conference on Management of Data, Washington DC, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. (CSUR) 38(3), 9 (2006)
Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right objective measure for association analysis. Inf. Syst. 29(4), 293–313 (2004)
Tew, C., Giraud-Carrier, C., Tanner, K., Burton, S.: Behavior-based clustering and analysis of interestingness measures for association rule mining. Data Min. Knowl. Disc. 28(4), 1004–1045 (2014)
Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, pp. 32–41 (2002)
McGarry, K.: A survey of interestingness measures for knowledge discovery. Knowl. Eng. 20(1), 39–61 (2005)
Vaillant, B., Lenca, P., Lallich, S.: A clustering of interestingness measures. In: Proceedings of the 7th international conference on discovery science (LNAI 3245), pp 290–297 (2004)
Huynh, X.H., Guillet. F., Briand, H.: Discovering the stable clusters between interestingness measures. In: Proceedings of the 8th International Conference on Enterprise Information Systems: Databases and Information Systems Integration, pp 196–201 (2006)
Ohsaki, M., Kitaguchi, S., Okamoto, K., Yokoi, H., Yamaguchi, T.: Evaluation of rule interestingness measures with a clinical dataset on hepatitis. In: Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (LNAI 3203), pp 362–373 (2004)
Somyanonthanakul, R., Theeramunkong, T.: An Investigation of Objective Interestingness Measures for Association Rule Mining. In: Pacific Rim International Conference on Artificial Intelligence. pp. 472–481. Springer International Publishing (2016)
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(2), 610–626 (2008)
Yao, Y., Zhong, N.: An analysis of quantitative measures associated with rules. In: Proceedings of the 3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining. LNCS, vol. 1574, pp 479–488 (1999)
Kannan, S., Bhaskaran, R.: Association rule pruning based on interestingness measures with clustering. Int. J. Comput. Sci. 6(1), 35–45 (2009)
Witten, I., Eibe, F.: Data mining: practical machine learning tools with Java
Acknowledgements
This work has been supported by Sirindhorn International Institute of Technology, Thammasat University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Somyanonthanakul, R., Theeramunkong, T. (2019). Dynamic Relation-Based Analysis of Objective Interestingness Measures in Association Rules Mining. In: Theeramunkong, T., et al. Advances in Intelligent Informatics, Smart Technology and Natural Language Processing. iSAI-NLP 2017. Advances in Intelligent Systems and Computing, vol 807. Springer, Cham. https://doi.org/10.1007/978-3-319-94703-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-94703-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94702-0
Online ISBN: 978-3-319-94703-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)