References
- 1. Agrawal, R., Imielinski, T., & Swami, A. (1993), “Database mining: a performance perspective”, IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 6, pp. 914-925.10.1109/69.250074
- 2. Agrawal, R., Srikant, R. (1994). “Fast Algorithms for Mining Association Rules”, in Bocca, J. B., Jarke, M., Zaniolo, C. (Eds.), Proceedings of 20th International Conference on Very Large Data Bases VLDB ‘94, September 12-15, 1994, Morgan Kaufmann, Santiago de Chile, Chile, pp. 487-499.
- 3. Chen, C. H., Lan, G. C., Hong, T. P., Lin, Y. K. (2013), “Mining high coherent association rules with consideration of support measure”, Expert Systems with Applications, Vol. 40, No. 16, pp. 6531–6537.10.1016/j.eswa.2013.06.002
- 4. European Commission (2012), “EuroStat, The Community Innovation Survey 2012”, available at http://ec.europa.eu/eurostat/documents/203647/203701/Harmonised+survey+questionnaire+2012/164dfdfd-7f97-4b98-b7b5-80d4e32e73ee (15 April 2015).
- 5. Heinrichs, J. H., Lim J. S. (2003), “Integrating web-based data mining tools with business models for knowledge management”, Decision Support Systems, Vol. 35, No. 1, pp. 103-112.10.1016/S0167-9236(02)00098-2
- 6. Javaheri, S. F., Sepehri, M. M., Teimourpour, B. (2013), “Response Modeling in Direct Marketing: A Data Mining-Based Approach for Target Selection”, in Zhao, Y., Cen J. (Eds.), Data Mining Applications with R, Amsterdam, Elsevier, pp. 153– 180.
- 7. Khan, D. M., Mohamudally, N., Babajee, D. K. R. (2013), “A Unified Theoretical Framework for Data Mining”, available at http://dx.doi.org/10.1016/j.procs.2013.05.015 (15 April 2015).
- 8. Kotler, P., Armstrong, G. (2010), Principles of marketing, Pearson Education.
- 9. Liao, C. W., Perng, Y. H., Chiang, T. L. (2009), “Discovery of unapparent association rules based on extracted probability”, Decision Support Systems, Vol. 47, No. 4, pp. 354–363.10.1016/j.dss.2009.04.006
- 10. Lin, K. C., Liao, I. E., Chen, Z. S. (2011), “An improved frequent pattern growth method for mining association rules”, Expert Systems with Applications, Vol. 38, No. 5, pp. 5154-5161.10.1016/j.eswa.2010.10.047
- 11. Liu, B., Ma, Y., Wong, C. K. (2001), “Classification Using Association Rules: Weaknesses and Enhancements”, in Grossman R. L. et al. (Eds.), Data Mining for Scientific and Engineering Applications, Springer, available at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.8.7943&rep=rep1&type=pdf (19 July 2014).
- 12. Masters, T. (1995), Advanced Algorithms for Neural Networks, A C++ Sourcebook, New York, USA, John Wiley & Sons, Inc.
- 13. Paliwal, M., Kumar, U. A. (2009), “Neural networks and statistical techniques: A review of applications”, Expert Systems with Applications, Vol. 36, pp. 2–17.10.1016/j.eswa.2007.10.005
- 14. Silberschatz, A., Tuzhilin, A. (1995), “On subjective measures of interestingness in knowledge discovery”, in Fayyad, U. M., Uthurusamy, R. (Eds.), Proceedings from the First International Conference on Knowledge Discovery and Data mining (KDD-95), August 20-21, 1995, Montreal, Canada, pp. 275-281.
- 15. Rygielski, C., Wang, J. C., Yen, D. C. (2002), “Data mining techniques for customer relationship management”, Technology in Society, Vol. 24, No. 4, pp. 483-502.10.1016/S0160-791X(02)00038-6
- 16. Shaw, M. J., Subramaniam, C., Tan, G. W., Welge M. E. (2001), “Knowledge management and data mining for marketing”, Decision Support Systems, Vol. 31, No. 1, pp. 127-137.10.1016/S0167-9236(00)00123-8
- 17. Tsai, H. H. (2013), “Knowledge management vs. data mining: Research trend, forecast and citation approach”, Expert Systems with Applications, Vol. 40, No. 8, pp. 3160–3173.10.1016/j.eswa.2012.12.029