Abstract
The objective interestingness measures play an important role in data mining because they are used for mining, filtering and ranking the patterns. However, there is no research that collects the measures fully as well as there is no tool that can: automatically calculate the interestingness values of the patterns by using those measures, and is the framework for rapidly developing the applications related to objective interestingness measures. This paper describes Interestingnesslab - a tool of the objective interestingness measures is developed in the R language. The main functions of the tool are: mining a set of association rules and presenting them by the cardinalities (\(n,n_{X},n_{Y},n_{X\overline{Y}}\)), calculating the interestingness value of an association rule according to 1 of 109 collected measures; calculating the interestingness values of the whole rule set in many measures selected by the user; discovering the tendencies in a data set and recommending the top N items to the user; and studying the specific behavior of a set of interestingness measures in the context of a specific dataset and in an exploratory data analysis perspective. With Interestingnesslab, the user can easily and quickly reuse its functions to develop his/her own applications.
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References
Huynh, H.X., Guillet, F., Le, T.Q., Briand, H.: Ranking objective interestingness measures with sensitivity values. VNU J. Sci. Nat. Sci. Technol. 24, 122–132 (2008)
McGarry, K.: A survey of interestingness measures for knowledge discovery. Knowl. Eng. Rev. J. 20(1), 39–61 (2005)
Guillet, F., Hamilton, H.Z.: Quality Measures in Data Mining. Series in Computational Intelligence, vol. 43. Springer, Heidelberg (2007)
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson (2006)
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3) (2006). Article 9
Guillaume, S., Grissa, D., Nguifo, E.M.: Categorization of interestingness measures for knowledge extraction. CoRR abs/1206.6741 (2012)
Grissa, D., Guillaume, S., Nguifo, E.M.: Combining Clustering techniques and FCA to characterize Interestingness Measures, Research Report LIMOS/RR-12-05 (2012)
Heravi, M.J., Zaïane, O.R.: A study on interestingness measures for associative classifiers. In: Proceedings of the 2010 ACM Symposium on Applied Computing, SAC 2010, pp. 1039–1046 (2010)
Huynh, X.H., Guillet, F., Briand, H.: ARQAT: an exploratory analysis tool for interestingness measures. In: Proceedings of the 11th International Symposium on Applied Stochastic Model and Data Analysis, ASMDA 2005, Brest, France, pp. 334–344 (2005)
Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Trans. Knowl. Data Eng. 8(6), 970–974 (1996)
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 Mining Knowl. Discov. 28(4), 1004–1045 (2014). Springer, US
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Appendix
Appendix
21 groups of measures, each group includes the measures called by the different names but having the same formula.
# | Group of measures | # | Group of measures |
|---|---|---|---|
1 | Accuracy, Causal Support | 2 | Added Value, Pavillon, Centered Confidence |
3 | Bayes Factor, Odd Multiplier | 4 | Correlation Coefficient, Phi-Coefficient, Pearson’s Correlation Coefficient, Linear-Correlation, Newrelevancy |
5 | Cosine, Ochia, IS Measure | 6 | Descriptive Confirmed-Confidence, Lerman Similarity Index |
7 | Dice Index, Czekanowski Dice, F-Measure Examples and Contra-Examples | 8 | Directed Contribution to Chi square, Lerman Similarity Index |
9 | Rate, Example and Contra-Example Rate, Encountered Rate | 10 | Gray and Orlowska’s Interestingness Weighting Dependency, I-Measure |
11 | Indice Probabilistc d’Ecart d’Equilibre, Probabilistic Measure of Deviation from Equilibrium(IPEE) | 12 | Jaccard, Coherence |
13 | Kappa Coefficient, Cohen | 14 | Kulczynski 1, Agreement-Disagreement Index |
15 | Lift, Interest | 16 | Loevinger, Certainty Factor, Satisfaction |
17 | Mutual Information, 2-way Support Variation | 18 | Normalized Difference, Match |
19 | Piatetsky-Shapiro, Pearl, Leverage 2, Carnap, Novelty | 20 | Relative Risk, Class Correlation Ratio |
21 | Specificity 1, Negative Reliability |
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Phan, L.P., Phan, N.Q., Nguyen, K.M., Huynh, H.H., Huynh, H.X., Guillet, F. (2017). Interestingnesslab: A Framework for Developing and Using Objective Interestingness Measures. In: Akagi, M., Nguyen, TT., Vu, DT., Phung, TN., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2016. Advances in Intelligent Systems and Computing, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-49073-1_33
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DOI: https://doi.org/10.1007/978-3-319-49073-1_33
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