Araştırma Makalesi
BibTex RIS Kaynak Göster

Market Basket Analysis with R programming Language: An Application on consumer purchasing behavior of a Supermarket in Muş

Yıl 2019, Cilt: 7 Sayı: 3, 89 - 97, 24.06.2019
https://doi.org/10.18506/anemon.462998

Öz

This study applies one of the tools of Data Mining, Association Rules
Analysis by using R programming language for a grocery located in the city
Mus/Turkey. Understanding consumers’ preferences and behaviors is very crucial
for the companies in terms of constructing optimal production and marketing
strategies. In addition to providing useful information to the company, this study
will also encourage researcher and retailers to use R language for analyzing
data with advanced algorithms. Furthermore, while finding the relationships
between the products, detailed product groups are used rather than general
product categories. The results of analyzes indicate that the most powerful
purchasing behavior is the rule of: the customers purchased eggs also purchased
some fruit or vegetable from the greengrocery.

Kaynakça

  • Aggarwal, C. C. (2015). Data Mining: The Textbook. Springer.
  • Agrawal, R., & Srikant, R. (1994). Fast Algorithms For Mining Association Rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, 487-499).
  • Aguinis, H., Forcum, L. E., & Joo, H. (2013). Using market basket analysis in management research. Journal of Management, 39(7), 1799-1824.
  • Akgün, A., & Çizel, B. (2016). Günlük Tur Programları Oluşturmada Veri Madenciliği: A Grubu Seyahat Acentası Örneği. Turar Turizm & Araştırma Dergisi, 6(1), 73-87.
  • Baralis, E., Cagliero, L., Cerquitelli, T., Garza, P., & Marchetti, M. (2011). CAS-Mine: providing personalized services in context-aware applications by means of generalized rules. Knowledge and information systems, 28(2), 283-310.
  • Bayardo Jr, R. J., & Agrawal, R. (1999, August). Mining the most interesting rules. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 145-154). ACM.
  • Berry, M. J. & G. Linoff. (1997). Data Mining Techniques: For Marketing, Sales, And Customer Support: John Wiley & Sons, Inc.
  • Bilgic, E.,& Çakır, Ö. (2018). Comparing Clusterings: A Store Segmentation Application. Erusosbilder, 32(44), 41-57
  • Budak, V. Ö., Kartal, E., & Gülseçen, S. (2018). Site-içi Aramalar ve Apriori Algoritması Kullanılarak Web Sitesi Ziyaretçilerinin İhtiyaç Tespitine Yönelik Bir Örnek Olay İncelemesi. Bilişim Teknolojileri Dergisi, 11(2), 211-222.
  • Chiang, W. Y. (2018). Applying Data Mining For Online Crm Marketing Strategy: An Empirical Case Of Coffee Shop İndustry in Taiwan. British Food Journal, 120(3), 665-675.
  • Cios, K. J., Pedrycz, W., Swiniarski, R. W., & Kurgan, L. A. (2007). Data Mining: A Knowledge Discovery Approach. Springer Science & Business Media.
  • Ertugrul, I., Oztas, T., Oztas, G. Z., & Ozcil, A. (2016). Shelf Layout With Integrating Data Mining And Multi-Dimensional Scaling. European Scientific Journal, ESJ, 12(10).
  • Guo, Y., Wang, M., & Li, X. (2017). Application Of An Improved Apriori Algorithm in a Mobile e-Commerce Recommendation System. Industrial Management & Data Systems, 117(2), 287-303.
  • Gulluoglu, S. S. (2015). Segmenting customers with data mining techniques. Digital Information, Networking, and Wireless Communications (DINWC), 2015, 154-159.
  • Giudici, P. (2005). Applied Data Mining: Statistical Methods For Business And Industry. John Wiley & Sons. Hahsler, M., & Chelluboina, S. (2011). Visualizing Association Rules: Introduction to the R-extension Package arulesViz. R project module, 223-238.
  • Hahsler, M., & Chelluboina, S. (2012). arulesViz: Visualizing Association Rules and Frequent Itemsets. R package version 0.1-5.
  • Han, J., Pei, J., & Kamber, M. (2011). Data Mining: Concepts And Techniques. Elsevier.
  • Han, J., Pei, J., & Yin, Y. (2000). Mining Frequent Patterns Without Candidate Generation. In ACM sigmod record (Vol. 29, No. 2, pp. 1-12). ACM.
  • Huseyinov, I., & Aytaç, U. C. (2017). Identification of Association Rules İn Buying Patterns Of Customers Based on Modified Apriori and Eclat Algorithms by Using R Programming Language. In Computer Science and Engineering (UBMK), 2017.
  • Kaur, M., & Kang, S. (2016). Market Basket Analysis: Identify The Changing Trends Of Market Data Using Association Rule Mining. Procedia Computer Science, 85, 78-85.
  • Kokoç, M., Aktepe, A., Ersöz, S., & Türker, A. K. (2016). Improvement of Facility Layout By Using Data Mining Algorithms And An Application. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 92-100.
  • Lee, D., Park, S. H., & Moon, S. (2013). Utility-Based Association Rule Mining: A Marketing Solution For Cross-Selling. Expert Systems with applications, 40(7), 2715-2725.
  • Leskovec, J., A. Rajaraman & J. D. Ullman. (2014). Mining Of Massive Datasets: Cambridge University Press.
  • Michael Hahsler, Bettina Gruen and Kurt Hornik (2005), arules - A Computational Environment for Mining Association Rules and Frequent Item Sets. Journal of Statistical Software 14/15.
  • Nengsih, W. (2015). A Comparative Study On Market Basket Analysis And Apriori Association Technique. In Information and Communication Technology (ICoICT)461-464, IEEE.
  • Özcan, T. & Ş. Esnaf. (2010). Perakende Endüstrisinde Raf Alanı Tahsis ve Mağaza Yerleşim Optimizasyonuna Bütünleşik Bir Model Önerisi. İÜ Mühendislik Bilimleri Dergisi. 1.1, 55-63.
  • Özçalıcı, M. (2017). Predicting Second-Hand Car Sales Price Using Decision Trees and Genetic Algorithms. Alphanumeric Journal, 5(1), 103-114.
  • Öztürk, G., & Tanrısevdi, A. (2017) Uluslararası Kruvaziyer Ziyaretçilerine Ait Özelliklerin Birliktelik Kuralı Modeli İle Analizi. Uluslararası İktisadi ve İdari Bilimler Dergisi, 3(1), 131-148.
  • Pehlivanoğlu, M. K., & Nevcihan, D. (2015). Veri Madenciliği Teknikleri Kullanılarak Ortaokul Öğrencilerinin Sosyal Ağ Kullanım Analizi: Kocaeli İli Örneği. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 3(2).
  • R Core Team (2018). R: A Language And Environment For Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
  • Sagin, A. N., & Ayvaz, B. (2018). Determination of Association Rules with Market Basket Analysis: Application in the Retail Sector. Southeast Europe Journal of Soft Computing, 7(1).
  • Sözen, E., Bardak, T., Peker, H., & Bardak, S.(2017).Apriori Algoritması Kullanılarak Mobilya Seçimde Etkili Olan Faktörlerin Analizi. İleri Teknoloji Bilimleri Dergisi, 6(3), 679-684.
  • Tekin, M., Y. Köse, Ö. Koyuncuoğlu & E. Tekin. (2015). The Analysis of Product Categories and Sales Relationships among Valuable Customers through Data Mining and Its Application to a National Retailer through Association Rules and Cluster Analysis. International Interdisciplinary Business-Economics Advancement Conference, 180.
  • Yıldırım, P., &Birant, D. (2018). Bulut Bilişimde Veri Madenciliği Tekniklerinin Uygulanması: Bir literatür taraması. Pamukkale University Journal of Engineering Sciences, 24(2).
  • Zaki, M. J., Parthasarathy, S., Ogihara, M., & Li, W. (1997, August). New Algorithms For Fast Discovery Of Association Rules. In KDD (Vol. 97, pp. 283-286).
  • Xie, Y., Cheng, J., Allaire, J. J., Reavis, B., Gersen, L., & Szopka, B. (2015). DT: a wrapper of the JavaScript library ‘DataTables. R package version 0.1.’Available at http://CRAN. R-project. org/package= DT [Verified 1 March 2016].

R Programlama Dili İle Pazar Sepet Analizi: Muş İl Merkezindeki Bir Süpermarkette Tüketicilerin Satın Alma Davranışlarının Tespiti Üzerine Bir Uygulama

Yıl 2019, Cilt: 7 Sayı: 3, 89 - 97, 24.06.2019
https://doi.org/10.18506/anemon.462998

Öz

Bu
araştırma, Veri Madenciliği tekniklerinden biri olan Birliktelik Kuralları Analizi’ni,
Muş ilinde faaliyet gösteren bir süpermarkete ait verilere R programlama dilini
kullanarak uygulamaktadır. Perakende sektöründe tüketicilerin tercihlerini,
satın alma davranışlarını anlamak, en uygun üretim ve pazarlama stratejileri
geliştirebilmek açısından çok önem arz etmektedir. Çalışma, araştırmaya konu
olan firmaya faydalı sonuçlar sağlamakla kalmayıp, hem araştırmacıların hem de perakendecilerin
sahip oldukları verileri gelişmiş algoritmalarla analiz edebilmeleri için
çalışma boyunca detaylı bir şekilde kodları paylaşılan R programlama dilini
kullanmaya teşvik edici niteliktedir. Ayrıca birçok çalışmadan farklı olarak
ürünler arasındaki ilişkiler bulunurken çok genel ürün gruplarıyla çalışmak
yerine daha ayrıntılı ürün gruplarıyla çalışılmıştır. Analizler sonucunda en
güçlü satın alma davranışının; yumurta alan müşterilerin manav reyonundan da alışveriş
yaptığı kuralı olduğu tespit edilmiştir.

Kaynakça

  • Aggarwal, C. C. (2015). Data Mining: The Textbook. Springer.
  • Agrawal, R., & Srikant, R. (1994). Fast Algorithms For Mining Association Rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, 487-499).
  • Aguinis, H., Forcum, L. E., & Joo, H. (2013). Using market basket analysis in management research. Journal of Management, 39(7), 1799-1824.
  • Akgün, A., & Çizel, B. (2016). Günlük Tur Programları Oluşturmada Veri Madenciliği: A Grubu Seyahat Acentası Örneği. Turar Turizm & Araştırma Dergisi, 6(1), 73-87.
  • Baralis, E., Cagliero, L., Cerquitelli, T., Garza, P., & Marchetti, M. (2011). CAS-Mine: providing personalized services in context-aware applications by means of generalized rules. Knowledge and information systems, 28(2), 283-310.
  • Bayardo Jr, R. J., & Agrawal, R. (1999, August). Mining the most interesting rules. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 145-154). ACM.
  • Berry, M. J. & G. Linoff. (1997). Data Mining Techniques: For Marketing, Sales, And Customer Support: John Wiley & Sons, Inc.
  • Bilgic, E.,& Çakır, Ö. (2018). Comparing Clusterings: A Store Segmentation Application. Erusosbilder, 32(44), 41-57
  • Budak, V. Ö., Kartal, E., & Gülseçen, S. (2018). Site-içi Aramalar ve Apriori Algoritması Kullanılarak Web Sitesi Ziyaretçilerinin İhtiyaç Tespitine Yönelik Bir Örnek Olay İncelemesi. Bilişim Teknolojileri Dergisi, 11(2), 211-222.
  • Chiang, W. Y. (2018). Applying Data Mining For Online Crm Marketing Strategy: An Empirical Case Of Coffee Shop İndustry in Taiwan. British Food Journal, 120(3), 665-675.
  • Cios, K. J., Pedrycz, W., Swiniarski, R. W., & Kurgan, L. A. (2007). Data Mining: A Knowledge Discovery Approach. Springer Science & Business Media.
  • Ertugrul, I., Oztas, T., Oztas, G. Z., & Ozcil, A. (2016). Shelf Layout With Integrating Data Mining And Multi-Dimensional Scaling. European Scientific Journal, ESJ, 12(10).
  • Guo, Y., Wang, M., & Li, X. (2017). Application Of An Improved Apriori Algorithm in a Mobile e-Commerce Recommendation System. Industrial Management & Data Systems, 117(2), 287-303.
  • Gulluoglu, S. S. (2015). Segmenting customers with data mining techniques. Digital Information, Networking, and Wireless Communications (DINWC), 2015, 154-159.
  • Giudici, P. (2005). Applied Data Mining: Statistical Methods For Business And Industry. John Wiley & Sons. Hahsler, M., & Chelluboina, S. (2011). Visualizing Association Rules: Introduction to the R-extension Package arulesViz. R project module, 223-238.
  • Hahsler, M., & Chelluboina, S. (2012). arulesViz: Visualizing Association Rules and Frequent Itemsets. R package version 0.1-5.
  • Han, J., Pei, J., & Kamber, M. (2011). Data Mining: Concepts And Techniques. Elsevier.
  • Han, J., Pei, J., & Yin, Y. (2000). Mining Frequent Patterns Without Candidate Generation. In ACM sigmod record (Vol. 29, No. 2, pp. 1-12). ACM.
  • Huseyinov, I., & Aytaç, U. C. (2017). Identification of Association Rules İn Buying Patterns Of Customers Based on Modified Apriori and Eclat Algorithms by Using R Programming Language. In Computer Science and Engineering (UBMK), 2017.
  • Kaur, M., & Kang, S. (2016). Market Basket Analysis: Identify The Changing Trends Of Market Data Using Association Rule Mining. Procedia Computer Science, 85, 78-85.
  • Kokoç, M., Aktepe, A., Ersöz, S., & Türker, A. K. (2016). Improvement of Facility Layout By Using Data Mining Algorithms And An Application. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 92-100.
  • Lee, D., Park, S. H., & Moon, S. (2013). Utility-Based Association Rule Mining: A Marketing Solution For Cross-Selling. Expert Systems with applications, 40(7), 2715-2725.
  • Leskovec, J., A. Rajaraman & J. D. Ullman. (2014). Mining Of Massive Datasets: Cambridge University Press.
  • Michael Hahsler, Bettina Gruen and Kurt Hornik (2005), arules - A Computational Environment for Mining Association Rules and Frequent Item Sets. Journal of Statistical Software 14/15.
  • Nengsih, W. (2015). A Comparative Study On Market Basket Analysis And Apriori Association Technique. In Information and Communication Technology (ICoICT)461-464, IEEE.
  • Özcan, T. & Ş. Esnaf. (2010). Perakende Endüstrisinde Raf Alanı Tahsis ve Mağaza Yerleşim Optimizasyonuna Bütünleşik Bir Model Önerisi. İÜ Mühendislik Bilimleri Dergisi. 1.1, 55-63.
  • Özçalıcı, M. (2017). Predicting Second-Hand Car Sales Price Using Decision Trees and Genetic Algorithms. Alphanumeric Journal, 5(1), 103-114.
  • Öztürk, G., & Tanrısevdi, A. (2017) Uluslararası Kruvaziyer Ziyaretçilerine Ait Özelliklerin Birliktelik Kuralı Modeli İle Analizi. Uluslararası İktisadi ve İdari Bilimler Dergisi, 3(1), 131-148.
  • Pehlivanoğlu, M. K., & Nevcihan, D. (2015). Veri Madenciliği Teknikleri Kullanılarak Ortaokul Öğrencilerinin Sosyal Ağ Kullanım Analizi: Kocaeli İli Örneği. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 3(2).
  • R Core Team (2018). R: A Language And Environment For Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
  • Sagin, A. N., & Ayvaz, B. (2018). Determination of Association Rules with Market Basket Analysis: Application in the Retail Sector. Southeast Europe Journal of Soft Computing, 7(1).
  • Sözen, E., Bardak, T., Peker, H., & Bardak, S.(2017).Apriori Algoritması Kullanılarak Mobilya Seçimde Etkili Olan Faktörlerin Analizi. İleri Teknoloji Bilimleri Dergisi, 6(3), 679-684.
  • Tekin, M., Y. Köse, Ö. Koyuncuoğlu & E. Tekin. (2015). The Analysis of Product Categories and Sales Relationships among Valuable Customers through Data Mining and Its Application to a National Retailer through Association Rules and Cluster Analysis. International Interdisciplinary Business-Economics Advancement Conference, 180.
  • Yıldırım, P., &Birant, D. (2018). Bulut Bilişimde Veri Madenciliği Tekniklerinin Uygulanması: Bir literatür taraması. Pamukkale University Journal of Engineering Sciences, 24(2).
  • Zaki, M. J., Parthasarathy, S., Ogihara, M., & Li, W. (1997, August). New Algorithms For Fast Discovery Of Association Rules. In KDD (Vol. 97, pp. 283-286).
  • Xie, Y., Cheng, J., Allaire, J. J., Reavis, B., Gersen, L., & Szopka, B. (2015). DT: a wrapper of the JavaScript library ‘DataTables. R package version 0.1.’Available at http://CRAN. R-project. org/package= DT [Verified 1 March 2016].
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makalesi
Yazarlar

Emrah Bilgiç 0000-0002-9875-2299

Yayımlanma Tarihi 24 Haziran 2019
Kabul Tarihi 23 Kasım 2018
Yayımlandığı Sayı Yıl 2019 Cilt: 7 Sayı: 3

Kaynak Göster

APA Bilgiç, E. (2019). R Programlama Dili İle Pazar Sepet Analizi: Muş İl Merkezindeki Bir Süpermarkette Tüketicilerin Satın Alma Davranışlarının Tespiti Üzerine Bir Uygulama. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 7(3), 89-97. https://doi.org/10.18506/anemon.462998

Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.