KABA KÜME YAKLAŞIMIYLA TÜKETİCİ DAVRANIŞLARININ ÖNGÖRÜLMESİ

Yıl 2019, Cilt: 41 Sayı: 1, 51 - 64, 12.07.2019

Nilgün Güler Bayazıt Yasemen Uçan

https://doi.org/10.14780/muiibd.582304

Cited By: 1

Öz

Bu çalışmada, tüketici davranışlarının kaba küme teorisine dayanan kural türetme algoritmalarıyla öngörülmesi amaçlanmıştır. Romanya Akademisi Dünya Ekonomisi Enstitüsü tarafından toplanan “Romanya - finansal okuryazarlık ve finansal hizmetler anketi” verileri kullanılmıştır. Çalışmada ön işlem olarak kaba küme teorisinin alt yaklaşım ve üst yaklaşım kümeleri kullanılarak veri kümesindeki gereksiz nitelikler elenmiş sadece vazgeçilemeyen niteliklerden oluşan çekirdek veri kümesi oluşturulmuştur. Çekirdek veri kümesine kural türetme algoritması olan LEM2 algoritması uygulanarak tüketici davranışlarını yüksek başarımla öngörebilen kurallar elde edilmiştir. Elde edilen sonuçlara göre kaba küme teorisine dayanan kural türetme algoritmaları tüketicilerin karar verme biçimleri ve davranış biçimlerinin öngörülmesini sağlayabilecek uygun bir araçtır.

Anahtar Kelimeler

Pazarlama, tüketici davranışı, kaba kümeler, kural tümevarım, veri madenciliği

Kaynakça

• ALPAYDIN, E. (2011). Yapay Öğrenme. İstanbul: Boğaziçi Üniversitesi Yayınevi.
• BADEA (STROIE), L. M. (2014). Predicting Consumer Behavior with Artificial Neural Networks, Emerging Market Queries in Finance and Business EMQFB2013, Procedia Economics and Finance 15: 238 –246
• BLASZCZYNSKI, J., Greco, S., Slowinski, R. (2007). Multi-criteria Classification – a New Scheme for Application of Dominance-based Decision Rules, European Journal of Operational Research 181: 1030–1044.
• CLARK, P., Niblett, T. (1989). The CN2 Induction Algorithm, Machine Learning, 3 (4): 261-283.
• CELOTTO, E., Ellero, A., Ferretti, P. (2012). Short-medium term tourist services demand forecasting with rough set theory. Procedia Economics and Finance, 3:62-67.
• CUI, N., Cui, Q. (2009). A Rough-Set Based Approach to Predict Consumers’ Brand Preference, International Workshop on Intelligent Systems and Applications, Wuhan, 1-4 .
• GRZYMALA-BUSSE J.W. (2009). Rule Induction, In: Maimon O., Rokach L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA:1-19.
• FURAJI, F., Latuszyńska, M., Wawrzyniak, A., Wasikowska, B. (2013). Study on the influence of advertising attractiveness on the purchase decisions of women and men., Journal of International Studies, 6(2): 20-32.
• HUANG, C., Yang, Y., Tzeng, G., Cheng, S., Lee, H. (2010). 4G Mobile Phone Consumer Preference Predictions by Using the Rough Set Theory and Flow Graphs, PICMET 2010 Technology Management for Global Economic Growth, Phuket: 1-10.
• LIAO, S. H., Chang, H. K. (2016). A rough set-based association rule approach for a recommendation system for online consumers, Information Processing & Management, 52(6):1142-1160.
• LIOU, J.J.H, Tzeng, G., (2010). A Dominance-based Rough Set Approach to Customer Behavior in the Airline Market, Information Sciences, 180 (11): 2230-2238.
• MICHALSKI, R. S., Mozetic, I., Hong, J., Lavrac, N. (1986). The AQ15 Inductive Learning System: an Overview and Experiments, Department of Computer Science, University of Illinois.
• PAWLAK, Z. (1982). Rough Sets, International Journal of Computer and Information Sciences, 11: 341-356.
• PAWLAK, Z. (2005). Rough Sets and Flow Graphs, Lecture Notes in Computer Science, 3641: 1-11.
• R Core Team (2018). R :A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. R version 3.5.0. https://www.R-project.org/ (Erişim Tarihi: 1.9.2018).
• RIZA, L. S., Janusz, A. (2015). RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories. R package version 1.3-0. https://CRAN.R-project.org/package=RoughSets (Erişim Tarihi: 1.9.2018).
• RUMELHART, D. E., Hinton, G. E., Williams, R. J. (1986), Learning Representation by Back-propagating Errors, Nature, 323, 533-536.
• QUINLAN, J.R. (1986). Induction of Decision Trees, Machine. Learning, 1:81–106.
• QUINLAN, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA.
• TRIPATHY, B.K. (2009). “Rough Sets on Fuzzy Approximation Spaces and Intuitionistic Fuzzy Approximation Spaces”, Rough Set Theory: A True Landmark in Data Analysis, Derleyen: Abraham A., Falcón R., Bello R., Series: Studies in Computational Intelligence, (174), Springer, Berlin, Heidelberg: 3-44.
• WALCZAK, B., Massart, D. L. (1999). Rough Sets Theory, Chemometrics and Intelligent Laboratory Systems, 47 (1): 1-16.
• ZADEH, L. A. (1965). Fuzzy sets, Information and control, 8(3): 338-353.
• ZHANG, Y., Zhao, Z., Yu, J., Wang, K. (2014). Application of rough sets in E-commerce consumer behavior prediction, Advanced Science and Technology Letters Vol.53 (ICM 2014): 255-260.