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Elektrik Malzemeleri Sektöründe R ve Shiny ile Pazar Sepet Analizine Yönelik Dinamik Bir Uygulama

Year 2019, Volume: 12 Issue: 2, 93 - 102, 30.04.2019
https://doi.org/10.17671/gazibtd.448245

Abstract

Bu çalışmada, birliktelik kurallarına ait algoritmalar kullanılarak, müşterilerin birlikte satın almayı tercih ettikleri ürünler tespit edilerek, müşteri ilişkileri yönetimine ilişkin bir uygulama gerçekleştirilmesi amaçlanmıştır. Çalışmada kullanılan veri seti elektrik sektöründe faaliyet gösteren bir firmadan temin edilmiştir. Veri analizi süresince Çarpraz Endüstri Standard Süreç Modeli (CRISP-DM) modeli takip edilmiştir. İki yılı kapsayan veriye, birliktelik kurallarından Apriori Algoritması uygulanmıştır. Veri analizleri R programlama dili ile gerçekleştirilmiştir. Kodların gerçekleştirilmesinde RStudio geliştirme ortamından yararlanılmıştır. Apriori Algoritması’ndan elde edilen model Shiny (shiny.apps.io) aracılığı ile web ortamına taşınmıştır. HESNYA-03 ve HESNYA-02 ürünleri arasındaki çapraz satış ve ürünün farklı renklerinin birlikte satışı, ürünü ön plana çıkarmaktadır. Buradan hareketle, çeşitli renk paletleri oluşturarak, müşterilerin bu ve benzeri paketlerin yanında yeni ürünler sunarak daha az tercih edilen ürünlerin alımlarını arttırabileceği ortaya konuşmuştur. Kullanıcıya analiz edilen veri setini sorgulama ve algoritma ile ilgili temel düzenlemeleri yapabilme imkânı verilmiştir. Böylelikle uygulamanın zaman ve mekândan bağımsız, dinamik bir hal alması sağlanmıştır.

References

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  • [20] C. Shearer, “The CRISP-DM Model: The New Blueprint for Data Mining”, Journal of Data Warehousing, 5, 2000.
  • [21] C. Zhang, S. Zhang, Association Rule Mining Models and Algorithms, Berlin: Springer, ISBN 3-540-43533-6, 2002.
  • [22] P. N. Tan, M. Steinbach, V. Kumar, “Association Analysis: Basic Concepts and Algorithms”, Introduction to Data Mining, 327–414, doi: https://doi.org/10.1111/j.1600-0765.2011.01426.x, 2005.
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  • [24] Internet: R: The R Project for Statistical Computing. (n.d.), https://www.r-project.org/, 05.01.2016.
  • [25] Internet: M. Hahsler, C. Buchta, B. Gruen, K. Hornik, I. Johnson, C. Borgelt, arules: Mining Association Rules and Frequent Itemsets, https://cran.r-project.org/web/packages=arules, 2019.
  • [26] Internet: M. Hahsler, G. Tyler, S. Chelluboina, arulesViz: Visualizing Association Rules and Frequent Itemsets, https://cran.r-project.org/web/packages/packages=arulesViz, 2018.
  • [27] Internet: RStudio – Open source and enterprise-ready professional software for R. (n.d.), https://www.rstudio.com/, 05.01.2016.
  • [28] M. E. Balaban, E. Kartal, Veri Madenciliği ve Makine Öğrenmesi, Çağlayan Kitabevi, ISBN: 978-975-436-089-9, 2015.
  • [29] Internet: Shiny. (n.d.), http://shiny.rstudio.com/, 10.01.2017.
  • [30] G. Karahan Adalı, Veri Madenciliğinde Birliktelik Yöntemleri Ve Müşteri Ilişkileri Yönetimine Ilişkin Bir Uygulama, Doktora Tezi, İstanbul Üniversitesi, Fen Bilimleri Enstitüsü, 2017.

A Dynamic Application of Market Basket Analysis with R and Shiny in The Electric Materials Sector

Year 2019, Volume: 12 Issue: 2, 93 - 102, 30.04.2019
https://doi.org/10.17671/gazibtd.448245

Abstract

In this study, it is aimed to determine the products that customers prefer to buy together by using algorithm of association rules Apriori, and to implement an application related to customer relationship management. The data set used in this study was obtained from a company operating in the electricity sector. CRoss-Industry Standard Process for Data Mining (CRISP-DM) model was used during data analysis. Association rules technique Apriori applied to data that contains the two year. Data analysis is performed with R language. RStudio was used as a development tool for R codes. The model performed with Apriori was transferred to web environment via Shiny (shinyapps.io). The cross sales realized between HESNYA-03 and HESNYA-02 products of this brand and the sales of different colors together bring the product into the forefront. From this result, it is thought that by creating various color packages related to the product, customers can increase purchases of less preferred products by being offered new products besides these and similar packages. The user is given the opportunity to query the analyzed data set and make basic arrangements related to the algorithm. This allows the application to be dynamic, independent of time and space.

References

  • [1] M. Karabatak, M. C. İnce, “Apriori Algoritması ile Öğrenci Başarısı Analizi”, Eleco`2004 Elektrik - Elektronik - Bilgisayar Mühendisliği Sempozyumu ve Fuarı, http://www.emo.org.tr/ekler/24f4c5eef7ec01c_ek.pdfursa, 2004.
  • [2] D. Birant, A. Kut, M. Ventura, H. Altınok, B. Altınok, E. Altınok, M. Ihlamur, “İş Zekası Çözümleri için Çok Boyutlu Birliktelik Kuralları Analizi”, 215–222, 2010.
  • [3] K. Tsiptsis, A. Chorianopoulos, Data Mining Techniques in CRM, 2009.
  • [4] T. C. Yang, H. Lai, “Comparison of product bundling strategies on different online shopping behaviors”, Electronic Commerce Research and Applications, 5(4), 295–304. doi: 10.1016/j.elerap.2006.04.006, 2006
  • [5] J. Han, M. Kamber, Data mining: Concepts and techniques. San Francisco: Morgan Kaufmann Publishers, 2006.
  • [6] C. F. Özçakır, Müşteri İşlemlerindeki Birlikteliklerin Belirlenmesinde Veri Madenciliği Uygulaması, Yüksek Lisans Tezi, Marmamara Üniversitesi, Fen Bilimleri Enstitüsü, 2006.
  • [7] Internet: S. Erpolat, Otomobil Yetkili Servislerinde Birliktelik Kurallarının Belirlenmesinde Apriori ve FP-Growth Algoritmalarının Karşılaştırılması, https://earsiv.anadolu.edu.tr/xmlui/handle/11421/163, 2012.
  • [8] P. Kumar, “Knowledge Discovery in Databases (KDD) with Images : A Novel Approach toward Image Mining and Processing”, International Journal of Computer Applications, 27(6), 10–13, 2011.
  • [9] D. Delen, G. Walker, A. Kadam, “Predicting breast cancer survivability: A comparison of three data mining methods”. Artificial Intelligence in Medicine, 34(2), 113–127, https://doi.org/10.1016/j.artmed.2004.07.002, 2005.
  • [10] M. M. Rashid, I. Gondal, I., J. Kamruzzaman, “Mining associated sensor patterns for data stream of wireless sensor networks”, Proceedings of the 8th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks - PM2HW2N ’13, New York, NewYork, USA, ACM Press, 91–98, https://doi.org/10.1145/2512840.2512853, 2013.
  • [10] R. Agrawal, J. C. Shafer, Parallel mining of association rules: Design, Implementation and Experience, IBM Research Report RJ 10004, 1996.
  • [11] S. Nasreen, M. A. Azam, K. Shehzad, U. Naeem, M. A. Ghazanfar, “Frequent pattern mining algorithms for finding associated frequent patterns for data streams: A survey”, Procedia Computer Science, 37, 109–116, doi: https://doi.org/10.1016/j.procs.2014.08.019, 2014.
  • [12] J. Nahar, T. Imam, K. S. Tickle, Y. P. P. Chen, H. H. Yang, S. Fong, Y. Zhao, “Association rule mining to detect factors which contribute to heart disease in males and females”, Expert Systems with Applications, 102(3), 335–351, doi: https://doi.org/10.1016/j.jss.2014.07.010, 2013.
  • [13] S. Palaniappan, R. Awang, “Intelligent Heart Disease Prediction System Using Data Mining Techniques”, International Journal of Computer Science and Network Security, 8, 343-350, 2008.
  • [14] C. H. Wen, S. H. Liao, W. L. Chang, P. Y. Hsu, “Mining shopping behavior in the Taiwan luxury products market”, Expert Systems with Applications, Elsevier Ltd, 39(12), 11257–11268, doi: 10.1016/j.eswa.2012.03.072, 2012.
  • [15] D. Ay, İ. Çil, “Mı̇gros türk a.ş.de bı̇rlı̇ktelı̇k kurallarinin yerleşı̇m düzenı̇ planlamada kullanilmasi”, 14–29, 2010.
  • [16] V. Ö. Budak, E. Kartal, S. Gülseçen, “Site-içi Aramalar ve Apriori Algoritması Kullanılarak Web Sitesi Ziyaretçilerinin İhtiyaç Tespitine Yönelik Bir Örnek Olay İncelemesi”, Bilişim Teknolojileri Dergisi, 11(2), 211-222, 2018.
  • [17] H. Sabnani, M. More, P. Kudale, S. Janrao, “Prediction of Student Enrolment Using Data Mining Techniques”, International Research Journal of Engineering and Technology (IRJET), 5(4), 1830-1833, 2018.
  • [18] C. Y. Tsai, H. Sheng, “A data mining approach to optimise shelf space allocation in consideration of customer purchase and moving behaviours”, International Journal of Production Research, 53(3), 850-866, doi: https://doi.org/10.1080/00207543.2014.937011, 2014.
  • [19] M. C. Chen, C. P. Lin, “A data mining approach to product assortment and shelf space allocation”, Expert Systems with Applications, 32(4), 976–986, doi: 10.1016/j.eswa.2006.02.001, 2007.
  • [20] C. Shearer, “The CRISP-DM Model: The New Blueprint for Data Mining”, Journal of Data Warehousing, 5, 2000.
  • [21] C. Zhang, S. Zhang, Association Rule Mining Models and Algorithms, Berlin: Springer, ISBN 3-540-43533-6, 2002.
  • [22] P. N. Tan, M. Steinbach, V. Kumar, “Association Analysis: Basic Concepts and Algorithms”, Introduction to Data Mining, 327–414, doi: https://doi.org/10.1111/j.1600-0765.2011.01426.x, 2005.
  • [23] R. Kaur, “Apriori algorithm for Mining Frequent Patterns using Parallel Computing : Survey”, International Journal, 6(5), 822-824, 2016.
  • [24] Internet: R: The R Project for Statistical Computing. (n.d.), https://www.r-project.org/, 05.01.2016.
  • [25] Internet: M. Hahsler, C. Buchta, B. Gruen, K. Hornik, I. Johnson, C. Borgelt, arules: Mining Association Rules and Frequent Itemsets, https://cran.r-project.org/web/packages=arules, 2019.
  • [26] Internet: M. Hahsler, G. Tyler, S. Chelluboina, arulesViz: Visualizing Association Rules and Frequent Itemsets, https://cran.r-project.org/web/packages/packages=arulesViz, 2018.
  • [27] Internet: RStudio – Open source and enterprise-ready professional software for R. (n.d.), https://www.rstudio.com/, 05.01.2016.
  • [28] M. E. Balaban, E. Kartal, Veri Madenciliği ve Makine Öğrenmesi, Çağlayan Kitabevi, ISBN: 978-975-436-089-9, 2015.
  • [29] Internet: Shiny. (n.d.), http://shiny.rstudio.com/, 10.01.2017.
  • [30] G. Karahan Adalı, Veri Madenciliğinde Birliktelik Yöntemleri Ve Müşteri Ilişkileri Yönetimine Ilişkin Bir Uygulama, Doktora Tezi, İstanbul Üniversitesi, Fen Bilimleri Enstitüsü, 2017.
There are 31 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Gökçe Karahan Adalı 0000-0001-8567-4626

M. Erdal Balaban This is me

Publication Date April 30, 2019
Submission Date July 26, 2018
Published in Issue Year 2019 Volume: 12 Issue: 2

Cite

APA Karahan Adalı, G., & Balaban, M. E. (2019). A Dynamic Application of Market Basket Analysis with R and Shiny in The Electric Materials Sector. Bilişim Teknolojileri Dergisi, 12(2), 93-102. https://doi.org/10.17671/gazibtd.448245