Presented at the 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences (ISAS WINTER-2019 (ENS)), Samsun, Turkey, Nov 22, 2019

SETSCI Conference Proceedings, 2019, 9, Page (s): 55-57 , https://doi.org/10.36287/setsci.4.6.021

Published Date: 22 December 2019    | 571     7

Furniture has a large share in all endstirisinde in the World and Turkey. People demand for furniture products is increasing day by day. Furniture manufacturers are curious about which furniture products are most demanded by customers. Although it is possible to reach data about the furniture product from the internet, it is very difficult to draw meaningful results from most of data. Web mining is a sub-branch of data mining and helps to draw meaningful results from web pages. With this information, companies can obtain important information that will provide superiority over their competitors. In this study, the best selling products from various web pages selling furniture were evaluated using web mining method. Rapidminer software was used for this purpose. With the web mining, the most used words or words in furniture products are determined from the web pages in the bestseller menu. As a result, it was found that the most commonly used word was the sofa. When we look at this result, it can be said that the most demand of furniture products is sofa. It is thought that the information obtained from the data obtained from this study will provide important information for future researchers.

Keywords - Furniture, product, bestseller, demand, web mining

[1] N. Çelik, “Türkiye’de Mobilya sektörü gelişim planı için bir karar modeli önerisi,” Sosyal ve Beşeri Bilimler Dergisi, cilt 4(1), sayfa 223-232, 2012.
[2] G. Bashimov, “Mobilya endüstrisi: Türkiye’nin küresel piyasadaki karşılaştırmalı üstünlüğü,” İktisadi Yenilik Dergisi cilt 4, Ocak 2017.
[3] B. Mobasher, R. Cooley, and J. Srivastava, “Automatic personalization based on web usage mining,” Communications of the ACM, vol. 43(8), pp. 142-151, 2000.
[4] A. Vahaplar, M. M. İnceoğlu, “Veri madenciliği ve elektronik ticaret”, Türkiye’de Internet Konferansları VII, 2001.
[5] R. Daş, “Web kullanıcı erişim kütüklerinden bilgi çıkarımı,”,Doktora Tezi, Fırat Üniversitesi Fen Bilimleri Enstitüsü, Elazığ, 2008.
[6] I. Çınar ve H. S. Bilge, “Web Madenciliği Yöntemleri ile Web Loglarının İstatistiksel Analizi ve Saldırı Tespiti,” Bilişim Teknolojileri Dergisi, cilt 9(2), sayfa 125-135, Mayıs 2016.
[7] H. A. Cuesta, D. L. Coffman, C. Branas, and H. M. Murphy, “Using decision trees to understand the ınfluence of ındividual- and neighborhood-level factors on urban diabetes and asthma,” Health & Place vol. 58, pp. 1-9, 2019.
[8] A. Naik, and L. Samant, “Correlation review of classification algorithm using data mining tool: weka, rapidminer, tanagra, orange and knime,” Procedia Computer Science, vol. 85 pp. 662–668, 2016.
[9] Z. E. Rasjid, and R. Setiawan, “Performance comparison and optimization of text document classification using k-nn and naïve bayes classification techniques,” Procedia Computer Science vol. 116 pp.107–112, 2017.
[10] P. Ristoski, C. Bizer, and H. Paulheim, “Mining the web of linked data with RapidMiner,” Journal of Web Semantics, vol. 35, pp. 142–51, 2015.