Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Proportional association based roi model

1. School of Mathematics and Information Sciences Guangzhou University, Guangzhou 510006, China;
2. Clearpier Inc. 1300-121 Richmond St. W. Toronto, Ontario M5H 2K1 Canada;
3. School of Mathematics and Information Sciences Guangzhou University, Guangzhou 510006, China

Based on a local-to-global proportional association measure proposed by Huang, Shi and Wang[9], with cost and revenue information known, an association measure is proposed to maximize the expected RoI. A descriptive experiment with a synthetical data set is presented.
  Article Metrics

Keywords Dimensioncost; proportional association matrix; return-on-investment; revenue

Citation: Wenxue Huang, Yuanyi Pan, Lihong Zheng. Proportional association based roi model. Big Data and Information Analytics, 2017, 2(2): 119-126. doi: 10.3934/bdia.2017004


  • [1] C. Cornforth, What makes boards effctive? an examination of the relationships between board inputs, structures, processes and effctiveness in non-profit organisations, Corporate Governance:An International Review, 9(2011), 217-227.
  • [2] L. L. Fong, M. S. Squillante and R. E. Hough, Computer resource proportional utilization and response time scheduling, US Patent, 6(2001), 263-359.
  • [3] L. A. Goodman, A single general method for the analysis of cross-classifed data:Reconciliation, and synthesis of some methods of pearson, yule, and fisher, and also some methods of correspondence analysis and association analysis, Journal of the American Statistical Association, 91(1996), 408-428.
  • [4] L. A. Goodman and W. H. Kruskal, Measures of Association for Cross Classifications, Springer, 1979.
  • [5] M. F. Gregor, L. Yang, E. Fabbrini, B. S. Mohammed, J. C. Eagon, G. S. Hotamisligil and S. Klein, Endoplasmic reticulum stress is reduced in tissues of obese subjects after weight loss, Diabetes, 58(2009), 693-700.
  • [6] W. Huang and Y. Pan, On balancing between optimal and proportional categorical predictions, Big Data and Information Analytics, 1(2016), 129-137.
  • [7] W. Huang, Y. Pan and J. Wu, Supervised discretization with GK-τ, Procedia Computer Science, 17(2013), 114-120.
  • [8] W. Huang, Y. Pan and J. Wu, Performance measures of rare events targeting, International Journal of Data Analysis Techniques and Strategies, 6(2014), 105-120.
  • [9] W. Huang, Y. Shi and X. Wang, A nominal association matrix with feature selection for categorical data, Comunications in Statistic-Theory and Methods, 46(2017), 7798-7819.
  • [10] H. Hwang, T. Jung and E. Suh, An ltv model and customer segmentation based on customer value:A case study on the wireless telecommunication industry, Expert Systems with Applications, 26(2004), 181-188.
  • [11] T. Lin, Y. Yang and H. T. Shiau, A work weighted state vector control method for geometrically nonlinear analysis, Computers and Structures, 46(1993), 689-694.
  • [12] C. X. Ling and C. Li, Data mining for direct marketing:Problems and solutions, in Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), AAAI Press, 1998, 73-79.
  • [13] J. R. Quinlan, Induction of decision trees, Machine Learning, 1(1986), 81-106.


Reader Comments

your name: *   your email: *  

Copyright Info: 2017, Wenxue Huang, Lihong Zheng, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved