References
- Amberg, N., & Fogarassy, C. (2019). Green consumer behavior in the cosmetics market. Resources, 8(3), 137. https://doi.org/10.3390/resources8030137
- Athanasoulias, G., & Chountalas, P. (2019). Increasing business intelligence through a CRM approach: An implementation scheme and application framework. International Journal of Information, Business and Management, 11(2), 146–178.
- Calitz, A., Bosire, S., & Cullen, M. (2018). The role of business intelligence in sustainability reporting for South African higher education institutions. International Journal of Sustainability in Higher Education, 19(7), 1185–1203. https://doi.org/10.1108/IJSHE-10-2016-0186
- Cebotarean, E. (2011). Business intelligence Journal of Knowledge Management. Economics and Information Technology, 1(2), 110.
- Deng, Z., Choi, K., Chung, F., & Wang, S. (2010). Enhanced soft subspace clustering integrating within cluster and between cluster information. Pattern Recognition, 43(3), 767–781. https://doi.org/10.1016/j.patcog.2009.09.010
- Deng, Z. H., Luo, K. H., & Yu, H. L. (2014). A study of supervised term weighting scheme for sentiment analysis. Expert Systems with Applications, 41(7), 3506–3513. https://doi.org/10.1016/j.eswa.2013.10.056
- Huang, J. Z., Ng, M. K., Rong, H., & Li, Z. (2005). Automated variable weighting K-means clustering. IEEE Transaction on Pattern Analysis and Machine Intelligence, 27(5), 657–667. https://doi.org/10.1109/TPAMI.2005.95
- Huh, M. H., & Lim, Y. B. (2009). Weighting variables in K-means clustering. Journal of Applied Statistics, 36(1), 67–78. https://doi.org/10.1080/02664760802382533
- Kahneman, D., & Thaler, R. H. (2006). Anomalies: Utility maximization and experienced utility. Journal of Economic Perspectives, 20(1), 221–234. https://doi.org/10.1257/089533006776526076
- Jin, D. H., & Kim, H. J. (2018). Integrated understanding of big data, big data analysis, and business intelligence: A case study of logistics. Sustainability, 10(10), 3778. https://doi.org/10.3390/su10103778
- Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700–710. https://doi.org/10.1016/j.ijinfomgt.2016.04.013
- Le, T. M., & Liaw, S. Y. (2017). Effects of pros and cons of applying big data analytics to consumers’ responses in an E-commerce context. Sustainability, 9(5), 798. https://doi.org/10.3390/su9050798
- Loewenstein, G., O’Donoghue, T., & Rabin, M. (2003). Projection bias in predicting future utility. The Quarterly Journal of Economics, 118(4), 1209–1248. https://doi.org/10.1162/003355303322552784
- Makarenkov, V., & Legendre, P. (2001). Optimal variable weighting for ultrametric and additive trees and K-means partitioning: Methods and software. Journal of Classification, 18(2), 245–271. https://doi.org/10.1007/s00357-001-0018-x
- Martin, A., Lakshmi, T. M., & Venkatesan, V. P. (2012). An analysis on business intelligence models to improve business performance. IEEE-ICAESM, Nagapattinam, pp. 503–508.
- Mehboob, Qadir, J., Ali, S., & Vasilakos, A. (2014). Genetic algorithms in wireless networking: Techniques, applications, and issues. arXiv:1411.5323v1 [cs.NI], 1–27.
- Modha, D. S., & Spangler, W. S. (2003). Feature weighting in k-means clustering. Machine Learning, 52(3), 217–237.
- Muller, H., & Freytag, J. C. (2015). Problems, methods, and challenges in comprehensive data cleansing. Humboldt-Universität.
- Nethravathi, P. S., & Karibasappa, K. (2016). Business intelligence appraisal of the customer dataset based on weighted correlation index. International Journal of Emerging Technology and Research, 3(6), 31–41.
- Nethravathi, P. S., & Karibasappa, K. (2017a). Augmentation of the customer’s profile dataset using Genetic Algorithm. International Journal of Research and Scientific Innovation (IJRSI), IV(VIS), 33–39.
- Nethravathi, P. S., & Karibasappa, K. (2017b). Business intelligence appraisal of augmented data based on existing customers’ dataset obtained by genetic algorithm using multiple correlation technique. IARJSET, 4(7), 81–85. https://doi.org/10.17148/IARJSET.2017.4713
- Nguefack-Tsague, G. (2014). Optimal weighting scheme in Model Averaging. American Journal of Applied Mathematics and Statistics, 2(3), 150–156. https://doi.org/10.12691/ajams-2-3-9
- Pilon, A. (2016). Hobbies survey: Most have made hobby related purchases. AYTM Company news - The Official Blog of Ask Your Target Market.
- Rasmussen, N. H., Goldy, P. S., & Solli, P. O. (2002). Financial business intelligence (pp. 304). John Wiley & Sons Inc.
- Sharma, A. K. (2013). Optimized test case generation using genetic algorithm. International Journal of Computing and Business Research (IJCBR), 4(3), 2229–6166.
- Shrivastava, A., & Lanjewar, U. (2011). Behavioural business intelligence framework based on online buying behaviour in Indian context: A knowledge management approach. International Journal of Computer Technology and Applications, 2(6), 3066–3078.
- Steinley, D. (2006). K-mens clustering: A half-century synthesis. British Journal of Mathematical and Statistical Psychology, 59(1), 1–34. https://doi.org/10.1348/000711005X48266
- Velasco, J. M., Garnica, O., & Contador, S. (2017). Data augmentation and evolutionary algorithms to improve the prediction of blood glucose levels in scarcity of training data. IEEE Congress on Evolutionary Computation (CEC).
- Vijayarani, S., & Sudha, S. (2013). Comparative analysis of classification function techniques for heart disease prediction. International Journal of Innovative Research in Co, 1(3), 4.
- Watson, H. J. (2009). Business intelligence – Past, present, and future. Communications of the Association for Information Systems, 25, 487–510. https://doi.org/10.17705/1CAIS.02539
- Xu, X., Huang, J. Z., & Ye, Y. (2013). TW-k-means: Automated two-level variable weighting clustering algorithm for multiview data. IEEE Transactions on Knowledge and Data Engineering, 25(4), 932–944. https://doi.org/10.1109/TKDE.2011.262
- Zhang, S., Li, S., Hu, J., Xing, H., & Zhu, W. (2019). An iterative algorithm for optimal variable weighting in K-means clustering. Communications in Statistics-Simulation and Computation, 48(5), 1346–1365. https://doi.org/10.1080/03610918.2017.1414244