Öz
Ülkeler arasındaki sınırların ortadan kalkması ile rekabet uluslararası boyuta taşınmıştır. Müşteri odaklı pazarlama anlayışı ile müşteriler işletmelerin en önemli varlıkları haline gelmiştir. Rekabetin küresel olarak devam etmesi ile mevcut müşterileri ve potansiyel müşterileri için farklı pazarlama faaliyetleri yürütmek işletmelerin sürekliliğini sağlamaları için bir zorunluluk haline gelmiştir. Müşterilerin ürün ve hizmet tercihleri ile ilgili doğru teslimatın gerçekleştirilebilmesi noktasında işletmelerin daha fazla müşteri verisine ihtiyacı ortaya çıkmıştır. Daha fazla veriye ulaşma ve verileri yorumlama bağlamında veri madenciliği işletmeler için önemli bir konu haline gelmiştir. Bu çalışma ile iOS ve Android işletim sistemine sahip cep telefonlarının teknik özelliklerinin birliktelik kuralları ile birlikte analiz edilmesi ve hangi teknik özelliklerin bir arada bulunduğunun tespit edilmesi amaçlanmıştır. Verilerin kaç kategoride kategorize edileceğine yönelik denemeler yapılmış ve veri seti en yüksek performans değerlerini ortaya çıkaran kategori numarasına ayrılmıştır. Elde edilen sonuçlar, cep telefonu pazarında bulunan ürünlerin özelliklerinin daha net anlaşılmasına yardımcı olacaktır.
Anahtar Kelimeler
Apriori Algoritması Pazar Sepeti Analizi Akıllı Telefon Pazarı
Kaynakça
- Agrawal, r., Srikant, R. (1994), “Fast algorithms for mining association rules in large databases”, Proceedings of the 20th International Conference on Very Large Data Bases, 487-499.
- Ahmad, W., Ahmed, T., & Ahmad, B. (2019). Pricing of mobile phone attributes at the retail level in a developing country: hedonic analysis. Telecommunications Policy, 43(4), 299-309.
- Bhatia, P. (2019). Data mining and data warehousing. principles and practical techniques. Cambridge Univesity Press.
- Cavique, L. (2007). A scalable algorithm for the market basket analysis. Journal of Retailing and Consumer Services. 14:400-407.
- Chen, Y. L., Tang, K., Shen, R. J., & Hu, Y. H. (2005). Market basket analysis in a multiple store environment. Decision support systems, 40(2), 339-354.
- Dewenter, R., Haucap, J., Luther, R., & Rötzel, P. (2007). Hedonic prices in the German market for mobile phones. Telecommunications Policy, 31(1), 4-13.
- Giudici, P., Figin, S. (2009), Applied data mining for business and industry, 2nd Ed. John Wiley & Sons Ltd., West Sussex. Gupta, S., & Mamtora, R. (2014). A survey on association rule mining in market basket analysis. International Journal of Information and Computation Technology, 4(4), 409-414.
- Ha, S. H., & Park, S. C. (1998). Application of data mining tools to hotel data mart on the Intranet for database marketing. Expert Systems with Applications, 15(1), 1-31. Hand, D., Mannila, H. and Smyth, P. (2001), Principles of Data Mining, The MIT Press, Cambridge.
- Hausman, J. (1999). Cellular telephone, new products, and the CPI. Journal of business & Economic Statistics, 17(2), 188-194.
- Hsieh, N. C. (2004). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications, 27(4), 623-633. Kamakura, W. A. (2012). Sequential market basket analysis. Marketing Letters, 23(3), 505-516.
- Kim, M. K., Wong, S. F., Chang, Y., & Park, J. H. (2016). Determinants of customer loyalty in the Korean smartphone market: Moderating effects of usage characteristics. Telematics and Informatics, 33(4), 936-949.
- 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.
- Li, Q., Zhang, Y., Kang, H., Xin, Y. and Shi, C. (2017). Mining association rules between stroke risk factors based on the apriori algorithm. Technology and Health Care, 25:S197-S205.
- Liang, Y. H. (2010). Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert systems with Applications, 37(12), 7489-7496.
- Montenegro, J. A., & Torres, J. L. (2016). Consumer preferences and implicit prices of smartphone characteristics (No. 2016-04).
- Monteserin, A., & Armentano, M. G. (2018). Influence-based approach to market basket analysis. Information Systems, 78, 214-224.
- Mostafavi, S. M., Roohbakhsh, S. S., & Behname, M. (2013). Hedonic price function estimation for mobile phone in Iran. International Journal of Economics and Financial Issues, 3(1), 202-205.
- Nisbet, R, Miner, G. and Yale, K. (2018). Handbook of statistical analysis and data mining applications. Elsevier Academic Press.
- Pande, A., & Abdel-Aty, M. (2009). Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool. Safety science, 47(1), 145-154.
- Raeder, T., & Chawla, N. V. (2011). Market basket analysis with networks. Social network analysis and mining, 1(2), 97-113.
- Raorane, A. A., Kulkarni, R. V., & Jitkar, B. D. (2012). Association rule–extracting knowledge using market basket analysis. Research Journal of Recent Sciences . ISSN, 2277, 2502.
- Santarcangelo, V., Farinella, G. M., Furnari, A., & Battiato, S. (2018). Market basket analysis from egocentric videos. Pattern Recognition Letters, 112, 83-90.
- Setiabudi, D. H., Budhi, G. S., Purnama, I. W. J., & Noertjahyana, A. (2011, August). Data mining market basket analysis' using hybrid-dimension association rules, case study in Minimarket X. In 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering (Vol. 1, pp. 196-199). IEEE.
- Setiawan, A., Budhi, G. S., Setiabudi, D. H., & Djunaidy, R. (2017, September). Data mining applications for sales information system using market basket analysis on stationery company. In 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT) (pp. 337-340). IEEE.
- Shmueli, G., Bruce, P.C., Yahav, I., Patel, N.R. Lichtendahl, K.C. (2018). Data Mining for Business Analytics. John Wiley & Sons, Inc.
- Trnka, A. (2010, June). Market basket analysis with data mining methods. In 2010 International Conference on Networking and Information Technology (pp. 446-450). IEEE.
- Wei, J. T., Lee, M. C., Chen, H. K., & Wu, H. H. (2013). Customer relationship management in the hairdressing industry: An application of data mining techniques. Expert Systems with Applications, 40(18), 7513-7518.
- Ziafat, H., & Shakeri, M. (2014). Using data mining techniques in customer segmentation. Journal of Engineering Research and Applications, 4(9), 70-79.
Öz
With the disappearance of the borders among the countries, competition has been moved to an international level. With the customer-oriented marketing understanding, customers have become the most important assets of the enterprises. With the global continuation of competition, carrying out different marketing activities for their current customers and potential customers has become a necessity for the enterprises to ensure continuity. At the point of being able to carry out correct delivery regarding the product and service preferences of the customers, the enterprises’ need for further customer data has emerged. In the context of reaching further data and interpreting the data, data mining has become an important topic for the enterprises. With this study, it has been aimed to analyze, with the association rules, the technical features of the mobile phones having iOS and Android operating systems and to identify which technical features appear together. Trials were conducted on in how many categories the data will be categorized and the dataset was discretized into the category number revealing the highest performance values. The achieved results will be helpful for a clearer understanding of the features of the products available in the mobile phone market.
Anahtar Kelimeler
A-priori Algorithm Market Basket Analysis Smart Phone Market
Kaynakça
- Agrawal, r., Srikant, R. (1994), “Fast algorithms for mining association rules in large databases”, Proceedings of the 20th International Conference on Very Large Data Bases, 487-499.
- Ahmad, W., Ahmed, T., & Ahmad, B. (2019). Pricing of mobile phone attributes at the retail level in a developing country: hedonic analysis. Telecommunications Policy, 43(4), 299-309.
- Bhatia, P. (2019). Data mining and data warehousing. principles and practical techniques. Cambridge Univesity Press.
- Cavique, L. (2007). A scalable algorithm for the market basket analysis. Journal of Retailing and Consumer Services. 14:400-407.
- Chen, Y. L., Tang, K., Shen, R. J., & Hu, Y. H. (2005). Market basket analysis in a multiple store environment. Decision support systems, 40(2), 339-354.
- Dewenter, R., Haucap, J., Luther, R., & Rötzel, P. (2007). Hedonic prices in the German market for mobile phones. Telecommunications Policy, 31(1), 4-13.
- Giudici, P., Figin, S. (2009), Applied data mining for business and industry, 2nd Ed. John Wiley & Sons Ltd., West Sussex. Gupta, S., & Mamtora, R. (2014). A survey on association rule mining in market basket analysis. International Journal of Information and Computation Technology, 4(4), 409-414.
- Ha, S. H., & Park, S. C. (1998). Application of data mining tools to hotel data mart on the Intranet for database marketing. Expert Systems with Applications, 15(1), 1-31. Hand, D., Mannila, H. and Smyth, P. (2001), Principles of Data Mining, The MIT Press, Cambridge.
- Hausman, J. (1999). Cellular telephone, new products, and the CPI. Journal of business & Economic Statistics, 17(2), 188-194.
- Hsieh, N. C. (2004). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications, 27(4), 623-633. Kamakura, W. A. (2012). Sequential market basket analysis. Marketing Letters, 23(3), 505-516.
- Kim, M. K., Wong, S. F., Chang, Y., & Park, J. H. (2016). Determinants of customer loyalty in the Korean smartphone market: Moderating effects of usage characteristics. Telematics and Informatics, 33(4), 936-949.
- 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.
- Li, Q., Zhang, Y., Kang, H., Xin, Y. and Shi, C. (2017). Mining association rules between stroke risk factors based on the apriori algorithm. Technology and Health Care, 25:S197-S205.
- Liang, Y. H. (2010). Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert systems with Applications, 37(12), 7489-7496.
- Montenegro, J. A., & Torres, J. L. (2016). Consumer preferences and implicit prices of smartphone characteristics (No. 2016-04).
- Monteserin, A., & Armentano, M. G. (2018). Influence-based approach to market basket analysis. Information Systems, 78, 214-224.
- Mostafavi, S. M., Roohbakhsh, S. S., & Behname, M. (2013). Hedonic price function estimation for mobile phone in Iran. International Journal of Economics and Financial Issues, 3(1), 202-205.
- Nisbet, R, Miner, G. and Yale, K. (2018). Handbook of statistical analysis and data mining applications. Elsevier Academic Press.
- Pande, A., & Abdel-Aty, M. (2009). Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool. Safety science, 47(1), 145-154.
- Raeder, T., & Chawla, N. V. (2011). Market basket analysis with networks. Social network analysis and mining, 1(2), 97-113.
- Raorane, A. A., Kulkarni, R. V., & Jitkar, B. D. (2012). Association rule–extracting knowledge using market basket analysis. Research Journal of Recent Sciences . ISSN, 2277, 2502.
- Santarcangelo, V., Farinella, G. M., Furnari, A., & Battiato, S. (2018). Market basket analysis from egocentric videos. Pattern Recognition Letters, 112, 83-90.
- Setiabudi, D. H., Budhi, G. S., Purnama, I. W. J., & Noertjahyana, A. (2011, August). Data mining market basket analysis' using hybrid-dimension association rules, case study in Minimarket X. In 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering (Vol. 1, pp. 196-199). IEEE.
- Setiawan, A., Budhi, G. S., Setiabudi, D. H., & Djunaidy, R. (2017, September). Data mining applications for sales information system using market basket analysis on stationery company. In 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT) (pp. 337-340). IEEE.
- Shmueli, G., Bruce, P.C., Yahav, I., Patel, N.R. Lichtendahl, K.C. (2018). Data Mining for Business Analytics. John Wiley & Sons, Inc.
- Trnka, A. (2010, June). Market basket analysis with data mining methods. In 2010 International Conference on Networking and Information Technology (pp. 446-450). IEEE.
- Wei, J. T., Lee, M. C., Chen, H. K., & Wu, H. H. (2013). Customer relationship management in the hairdressing industry: An application of data mining techniques. Expert Systems with Applications, 40(18), 7513-7518.
- Ziafat, H., & Shakeri, M. (2014). Using data mining techniques in customer segmentation. Journal of Engineering Research and Applications, 4(9), 70-79.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | İşletme |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Erken Görünüm Tarihi | 19 Haziran 2023 |
| Yayımlanma Tarihi | 22 Haziran 2023 |
| Gönderilme Tarihi | 19 Ekim 2022 |
| Kabul Tarihi | 17 Nisan 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 19 Sayı: 2 |
