JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Generally non-linear regression model containing standardized lift for association number estimation
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Generally non-linear regression model containing standardized lift for association number estimation
Park, Hee Chang;
  PDF(new window)
 Abstract
Among data mining techniques, the association rule is one of the most used in the real fields because it clearly displays the relationship between two or more items in large databases by quantifying the relationship between the items. There are three primary quality measures for association rule; support, confidence, and lift. We evaluate association rules using these measures. The approach taken in the previous literatures as to estimation of association rule number has been one of a determination function method or a regression modeling approach. In this paper, we proposed a few of non-linear regression equations useful in estimating the number of rules and also evaluated the estimated association rules using the quality measures. Furthermore we assessed their usefulness as compared to conventional regression models using the values of regression coefficients, F statistics, adjusted coefficients of determination and variation inflation factor.
 Keywords
Confidence;generally non-linear regression equation;interestingness measure;standardized lift;support;
 Language
Korean
 Cited by
 References
1.
Agrawal, R., Imielinski, R. and Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD Conference on Management of Data, Association for Computing Machinery, New York, USA.

2.
Cho, K. H. and Park, H. C. (2013). A study of Gyungnam's social indicator survey using data mining. Journal of the Korean Data Analysis Society, 15, 2489-2497.

3.
Geng, L. and Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys, 38, 1-32. crossref(new window)

4.
Han, G. and Jin, S. (2014). Introduction to big data and the case study of its application. Journal of the Korean Data Analysis Society, 16, 2447-2455.

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.
Lee, C. H. and Bae, J. H. (2014). A new importance measure of association rules using information theory. Journal of the Korea Information Processing Society Transactions on Software and Data Engineering, 3, 37-42.

7.
Lim, J., Lee, K. and Cho, Y. (2010). A study of association rule by considering the frequency. Journal of the Korean Data & Information Science Society, 21, 1061-069.

8.
Park, H. C. (2010a). Development of associative rank decision function using basic association rule thresholds. Journal of the Korean Data Analysis Society, 12, 961-972.

9.
Park, H. C. (2010b). Association rule ranking function by decreased lift influence. Journal of the Korean Data & Information Science Society, 21, 397-405.

10.
Park, H. C. (2010c). Association rule ranking function using conditional probability increment ratio. Journal of the Korean Data & Information Science Society, 21, 709-717.

11.
Park, H. C. (2010d). Association rule ranking function using standardized lift. Journal of the Korean Data Analysis Society, 12, 2661-2670.

12.
Park, H. C. (2011a). Proposition of negatively pure association rule threshold. Journal of the Korean Data & Information Science Society, 22, 179-188.

13.
Park, H. C. (2011b). The proposition of attributably pure confidence in association rule mining. Journal of the Korean Data & Information Science Society, 22, 235-243.

14.
Park, H. C. (2013a). A study on comparison of non-linear regression model for decision of association rule numbers. Journal of the Korean Data Analysis Society, 15, 125-132.

15.
Park, H. C. (2013b). Non-linear regression model considering all association thresholds for decision of association rule numbers. Journal of the Korean Data & Information Science Society, 24, 267-275. crossref(new window)

16.
Park, H. C. (2014a). Comparison of confidence measures useful for classification model building. Journal of the Korean Data & Information Science Society, 25, 1-7. crossref(new window)

17.
Park, H. C. (2014b). Development of regression models by standardized lift for association rule number estimation. Journal of the Korean Data Analysis Society, 16, 2447-2455.

18.
Park, H. C. (2015). A study on the ordering of PIM family similarity measures without marginal probability. Journal of the Korean Data & Information Science Society, 26, 367-376. crossref(new window)

19.
Silberschatz, A. and Tuzhilin, A. (1996). What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge Data Engineering, 8, 970-974. crossref(new window)

20.
Tan, P. N., Kumar, V. and Srivastava, J. (2002). Selecting the right interestingness measure for association patterns. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, New York, USA.

21.
Wu, X., Zhang, C. and Zhang, S. (2004). Efficient mining of both positive and negative association rules. ACM Transactions on Information Systems, 22, 381-405. crossref(new window)

22.
Yi, W., Lu, M. and Liu, Z. (2011). Regression analysis in the number of association rules. International Journal of Automation and Computing, 8, 78-82. crossref(new window)