Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Generally non-linear regression model containing standardized lift for association number estimation

연관성 규칙 수의 추정을 위한 일반적인 비선형 회귀모형에서의 표준화 향상도 활용 방안

  • Received : 2016.04.21
  • Accepted : 2016.05.21
  • Published : 2016.05.31
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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.

최근에 많이 활용되고 있는 데이터 분석을 위한 연관성 규칙 마이닝은 대용량 데이터베이스에 많이 활용되고 있는 서 두 항목간의 관계를 측도화 함으로써 두 개 이상의 항목간의 관련성을 표시하여 주는 기법이다. 연관성 규칙의 여부를 판단하기 위한 연관성 평가 기준에는 지지도, 신뢰도, 그리고 향상도 등이 있으며, 이들 세 가지 기준을 이용하여 연관성 규칙 생성 여부를 판단하게 된다. 이에 대한 기존의 연구 결과는 결정함수를 이용하는 방법과 회귀모형을 이용하는 방법으로 분류할 수 있다. 회귀모형을 이용하여 수행한 연구에는 지지도와 신뢰도에 의한 모형, 세 가지 평가 기준의 쌍에 의한 모형, 표준화 향상도를 포함한 세 가지 평가 기준의 쌍에 의한 모형, 그리고 세 가지 평가 기준 전부를 고려한 모형 등이 있다. 본 논문에서는 기존의 연구를 확장하는 의미에서 표준화 향상도를 포함한 세가지 평가 기준 전부를 고려한 비선형 회귀모형을 이용하여 연관성 규칙의 수를 추정하는 방안에 대해 강구하고자 한다. 또한 분산분석에서의 F 통계량과 수정 결정계수를 이용하여 각 모형의 유의한 정도를 비교하는 동시에 분산팽창계수에 의한 공선성 문제를 진단함으로써 가장 유용한 회귀 모형을 탐색하고자 한다.

Keywords

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. https://doi.org/10.1145/1132952.1132953
  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. https://doi.org/10.7465/jkdi.2013.24.2.267
  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. https://doi.org/10.7465/jkdi.2014.25.1.1
  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. https://doi.org/10.7465/jkdi.2015.26.2.367
  19. Silberschatz, A. and Tuzhilin, A. (1996). What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge Data Engineering, 8, 970-974. https://doi.org/10.1109/69.553165
  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. https://doi.org/10.1145/1010614.1010616
  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. https://doi.org/10.1007/s11633-010-0557-x