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http://dx.doi.org/10.7465/jkdi.2015.26.3.611

Proposition of balanced comparative confidence considering all available diagnostic tools  

Park, Hee Chang (Department of Statistics, Changwon National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.26, no.3, 2015 , pp. 611-618 More about this Journal
Abstract
By Wikipedia, big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Data mining is the computational process of discovering patterns in huge data sets involving methods at the intersection of association rule, decision tree, clustering, artificial intelligence, machine learning. Association rule is a well researched method for discovering interesting relationships between itemsets in huge databases and has been applied in various fields. There are positive, negative, and inverse association rules according to the direction of association. If you want to set the evaluation criteria of association rule, it may be desirable to consider three types of association rules at the same time. To this end, we proposed a balanced comparative confidence considering sensitivity, specificity, false positive, and false negative, checked the conditions for association threshold by Piatetsky-Shapiro, and compared it with comparative confidence and inversely comparative confidence through a few experiments.
Keywords
Association rule; balanced comparative confidence; big data; comparative confidence; inversely comparative confidence;
Citations & Related Records
Times Cited By KSCI : 11  (Citation Analysis)
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1 Ahn, K. and Kim, S. (2003). A new interestingness measure in association rules mining. Journal of the Korean Institute of Industrial Engineers, 29, 41-48.
2 Berzal, F., Blanco, I., Sanchez, D. and Vila, M. (2001). A new framework to assess association rules. Proceedings of the 4th International Conference on Intelligent Data Analysis, 95-104.
3 Hilderman, R. J. and Hamilton, H. J. (2000). Applying objective interestingness measures in data mining systems. Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, 432-439.
4 Hwang, J. and Kim, J. (2003). Target marketing using inverse association rule. Journal of Intelligence and Information Systems, 9, 195-209.
5 Jin, D. S., Kang, C., Kim, K. K. and Choi, S. B. (2011). CRM on travel agency using association rules. Journal of the Korean Data Analysis Society, 13, 2945-2952.
6 Kim, T. (2002). Estimation of defect rate from the screening test - The case of unknown sensitivity and specificity. Journal of the Korean Society for Quality Management, 30, 144-151.
7 Kuo, Y. T. (2009) Mining surprising patterns, The doctoral paper of Melbourne university, Australia.
8 Lavrac, N., Flach, P. and Zupan, B. (1999). Rule evaluation measures: a unifying view. Proceedings of the 9th International Workshop on Inductive Logic Programming, 174-185.
9 Liu, B., Hsu, W., Chen, S. and Ma, Y. (2000). Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems, 15, 47-55.   DOI   ScienceOn
10 McNicholas, P.D., Murphy, T.B. and O'Regan, O. (2008). Standardising the lift of an association rule. Computational Statistics and Data Analysis, 52, 4712-4721.   DOI   ScienceOn
11 Park, H. C. (2011a). The proposition of attributably pure confidence in association rule mining. Journal of the Korean Data & Information Science Society, 22, 235-243.
12 Park, H. C. (2011b). Proposition of symmetrically pure confidence in association rule discovery. Journal of the Korean Data Analysis Society, 13, 879-890.
13 Park, H. C. (2014a). Comparison of confidence measures useful for classification model building. Journal of the Korean Data & Information Science Society, 25, 365-371.   DOI   ScienceOn
14 Park, H. C. (2012). Exploration of symmetric similarity measures by conditional probabilities as association rule thresholds. Journal of the Korean Data Analysis Society, 14, 707-716.
15 Park, H. C. (2013a). The proposition of c ompared and a ttributably pure confidence in association rule mining. Journal of the Korean Data & Information Science Society, 24, 523-532.   DOI   ScienceOn
16 Park, H. C. (2013b). A proposition of association rule thresholds considering relative occurrence/ nonoccurrence. Journal of the Korean Data Analysis Society, 15, 1841-1850.
17 Park, H. C. (2014b). Proposition of causally confirmed measures in association rule mining. Journal of the Korean Data & Information Science Society, 25, 857-868.   DOI   ScienceOn
18 Park, H. C. (2014c). Development of association rule threshold by balancing of relative rule accuracy. Journal of the Korean Data & Information Science Society, 25, 1345-1352.   DOI   ScienceOn
19 Piatetsky-Shapiro, G. (1991). Knowledge discovery in databases, MIT Press, Cambridge.