Prediction of the Shoppers Loyalty with Aggregated Data Streams

[1] A. Agarwal, O. Chapelle, M. Dudik and J. Langford. A Reliable Effective Terascale Linear Learning System. Journal of Machine Learning Research, 15, 2014, pp. 1111 - 1133.Search in Google Scholar

[2] V. Bhambri. Data Mining as a Tool to Predict Churn Behaviour of Customers. International Journal of Management Research, April 2013, pp. 59 - 69.Search in Google Scholar

[3] P. Domingos and G. Hulten. Mining high-speed data streams. KDD 2000, pp. 71 - 80.10.1145/347090.347107Search in Google Scholar

[4] P. Dhandayudam and I. Krishnamurthi. Customer Behavior Analysis Using Rough Set Approach. Journal of Theoretical and Applied Electronic Commerce Research. ISSN 0718-1876 Electronic Version, Universidad de Talca - Chile, Vol. 8(2), 2013, pp. 21-33.10.4067/S0718-18762013000200003Search in Google Scholar

[5] R. East, P. Gendall, K. Hammond and W. Lomax. Consumer Loyalty: Singular, Additive or Interactive? Australasian Marketing Journal, 13(2), 2005, pp. 10 - 26.10.1016/S1441-3582(05)70074-4Search in Google Scholar

[6] A. Karahoca, D. Karahoca and N. Aydin. Benchmarking the Data Mining Algorithms with Adaptive Neuro-Fuzzy Inference System in GSM Churn Management. Data Mining and Knowledge Discovery in Real Life Applications, Book edited by: Julio Ponce and Adem Karahoca, 2009, Vienna, Austria, pp. 229 - 242.10.5772/6451Search in Google Scholar

[7] C.-S. Lin, G.-H. Tzeng, Y.-C. Chin. Combined rough set theory and flow network graph to predict customer churn in credit card accounts. Expert Systems with Applications, 38, 2011, pp. 8 - 15.10.1016/j.eswa.2010.05.039Search in Google Scholar

[8] H. McMahan, G. Holt, D. Sculley, M. Young, D. Ebner, J. Grady, L. Nie, T. Phillips, E. Davydov, D.Golovin, S. Chikkerur, D. LiuM. Wattenberg, A.Hrafnkelsson, T. Boulos, J. Kubica, Ad Click Prediction: a View from the Trenches, KDD ’13 Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, Pp. 1222-1230, ACM New York, NY, USA 201310.1145/2487575.2488200Search in Google Scholar

[9] V. Migueis, D. den Poel, A. Camanho, J. Cunha. Modeling partial customer churn: On the value of first product-category purchase sequences. Expert Systems with Applications, 39, 2012, pp. 11250 -11256.10.1016/j.eswa.2012.03.073Search in Google Scholar

[10] T. Mirzaei and L. Lyer. Application of Predictive Analytics in Customer Relationship Management: a Literature Review and Classification. Proceedings of the Southern Association for Information Systems Conference, Macon, GA, USA March 21st - 22nd, 2014.Search in Google Scholar

[11] N. Hashmi, N. Butt and M. Iqbal. Customer Churn Prediction in Telecommunication A Decade Review and Classification. International Journal of Computer Science Vol. 10(5), 2013.Search in Google Scholar

[12] W. Hu. Developing pertubation rate of the rough set theory to evaluate the electronic transaction quality of on-line shopping. Pakistan Journal of Statistics, 2012 Vol. 28(5), pp. 581-596.Search in Google Scholar

[13] J. Liou, G.-H. Tzeng. A Dominance-based Rough Set Approach to customer behavior in the airline market . Information Sciences, 2010, 180, pp. 2230-2238.10.1016/j.ins.2010.01.025Search in Google Scholar

[14] S. Neslin, S. Gupta, W. Kamakura, J. Lu, and C. Mason. Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models. Journal of Marketing Research, 43, 2006, pp. 204 - 211.10.1509/jmkr.43.2.204Search in Google Scholar

[15] E.W.T. Ngai, L. Xiu, D.C.K. Chau. Application of data mining techniques in customer relationship management: a literature review and classification. Expert Systems with Applications, 36, 2009, pp. 2592 - 2602.10.1016/j.eswa.2008.02.021Search in Google Scholar

[16] V. Nikulin. Classification of Imbalanced Data with Random Sets and Mean-Variance Filtering. International Journal of Data Warehousing and Mining, 4(2), 2008, pp. 63 - 78.10.4018/jdwm.2008040108Search in Google Scholar

[17] V. Nikulin, A. Bakharia and T.-H. Huang. On the Evaluation of the Homogeneous Ensembles with CV-passports. LNCS 7867, Springer, J.Li et al. (Eds.), PAKDD 2013 Workshops, pp. 109 - 120.10.1007/978-3-642-40319-4_10Search in Google Scholar

[18] V. Nikulin. Hybrid Recommender System for Prediction of the Yelp Users Preferences. ICDM 2014, St.Petersburg, Russia. LNAI 8557, Springer, P. Perner (Eds.), pp. 85 - 99.10.1007/978-3-319-08976-8_7Search in Google Scholar

[19] B. Pal, R. Sinha, A. Saha, P. Jaumann and S. Misra. Customer Targeting Framework: Scalable Repeat Purchase Scoring Algorithm for Large Databases. Proceedings of 2012 4th International Conference on Machine Learning and Computing IPCSIT, vol. 25, 2012 IACSIT Press, Singapore, pp. 143 - 146.Search in Google Scholar

[20] Z. Pawlak. Rough set. International Journal of Computer and Information Sciences, 11(1), 1982, pp. 341 - 356.10.1007/BF01001956Search in Google Scholar

[21] K. Coussement, D. Van den Poel. Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers. Expert Systems with Applications, 36, 2009, pp. 6127 - 6134.10.1016/j.eswa.2008.07.021Search in Google Scholar

[22] A. Sharma and P. K. Panigrahi. A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services. International Journal of Computer Applications, 27(11), 2011, pp. 26 - 31.10.5120/3344-4605Search in Google Scholar

[23] Y. Xie, Xiu Li, E.W.T. Ngai, W. Ying. Customer churn prediction using improved balanced random forests. Expert Systems with Application, 36, 2009, pp. 5445 - 5449. 10.1016/j.eswa.2008.06.121Search in Google Scholar