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KABA KÜME YAKLAŞIMIYLA TÜKETİCİ DAVRANIŞLARININ ÖNGÖRÜLMESİ

Year 2019, , 51 - 64, 12.07.2019

Nilgün Güler Bayazıt Yasemen Uçan

https://doi.org/10.14780/muiibd.582304
Cited By: 1

Abstract

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.

Keywords

Pazarlama tüketici davranışı kaba kümeler kural tümevarım veri madenciliği

References

  • 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.

Year 2019, , 51 - 64, 12.07.2019

Nilgün Güler Bayazıt Yasemen Uçan

https://doi.org/10.14780/muiibd.582304
Cited By: 1

Abstract

References

  • 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.
There are 23 citations in total.

Details

Primary Language Turkish
Journal Section Makaleler
Authors

Nilgün Güler Bayazıt This is me 0000-0003-0221-294X

Yasemen Uçan This is me

Publication Date July 12, 2019
Submission Date February 1, 2019
Published in Issue Year 2019

Cite

APA Güler Bayazıt, N., & Uçan, Y. (2019). KABA KÜME YAKLAŞIMIYLA TÜKETİCİ DAVRANIŞLARININ ÖNGÖRÜLMESİ. Marmara Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 41(1), 51-64. https://doi.org/10.14780/muiibd.582304
AMA Güler Bayazıt N, Uçan Y. KABA KÜME YAKLAŞIMIYLA TÜKETİCİ DAVRANIŞLARININ ÖNGÖRÜLMESİ. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi. July 2019;41(1):51-64. doi:10.14780/muiibd.582304
Chicago Güler Bayazıt, Nilgün, and Yasemen Uçan. “KABA KÜME YAKLAŞIMIYLA TÜKETİCİ DAVRANIŞLARININ ÖNGÖRÜLMESİ”. Marmara Üniversitesi İktisadi Ve İdari Bilimler Dergisi 41, no. 1 (July 2019): 51-64. https://doi.org/10.14780/muiibd.582304.
EndNote Güler Bayazıt N, Uçan Y (July 1, 2019) KABA KÜME YAKLAŞIMIYLA TÜKETİCİ DAVRANIŞLARININ ÖNGÖRÜLMESİ. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi 41 1 51–64.
IEEE N. Güler Bayazıt and Y. Uçan, “KABA KÜME YAKLAŞIMIYLA TÜKETİCİ DAVRANIŞLARININ ÖNGÖRÜLMESİ”, Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, vol. 41, no. 1, pp. 51–64, 2019, doi: 10.14780/muiibd.582304.
ISNAD Güler Bayazıt, Nilgün - Uçan, Yasemen. “KABA KÜME YAKLAŞIMIYLA TÜKETİCİ DAVRANIŞLARININ ÖNGÖRÜLMESİ”. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi 41/1 (July 2019), 51-64. https://doi.org/10.14780/muiibd.582304.
JAMA Güler Bayazıt N, Uçan Y. KABA KÜME YAKLAŞIMIYLA TÜKETİCİ DAVRANIŞLARININ ÖNGÖRÜLMESİ. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2019;41:51–64.
MLA Güler Bayazıt, Nilgün and Yasemen Uçan. “KABA KÜME YAKLAŞIMIYLA TÜKETİCİ DAVRANIŞLARININ ÖNGÖRÜLMESİ”. Marmara Üniversitesi İktisadi Ve İdari Bilimler Dergisi, vol. 41, no. 1, 2019, pp. 51-64, doi:10.14780/muiibd.582304.
Vancouver Güler Bayazıt N, Uçan Y. KABA KÜME YAKLAŞIMIYLA TÜKETİCİ DAVRANIŞLARININ ÖNGÖRÜLMESİ. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2019;41(1):51-64.

Cited By

Ekonomi okuryazarlığının tüketici karar verme tarzlarına etkisi: Balıkesir'de bir uygulama
Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi
https://doi.org/10.31795/baunsobed.1317444

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