References
- An, H., & Park, M. (2020). Approaching fashion design trend applications using text mining and semantic network analysis. Fashion and Textiles, 7(1), 34. doi:10.1186/s40691-020-00221-w
- Berry, M. J. A., & Linoff, G. S. (2004). Data mining techniques: For marketing, sales, and customer relationship management (2nd ed.). New Jersey: John Wiley & Sons.
- Berson, A., Smith, S., & Thearling, K. (1999). Building data mining applications for CRM. New York: McGraw-Hill.
- Choi, K., & Nam, K. (2019). Analysis of shopping website visit types and shopping pattern. Journal of Intelligence and Information Systems, 25(1), 85-107. doi:10.13088/jiis.2019.25.1.085
- Choi, Y.-H., & Lee, K.-H. (2020). Informatics analysis of consumer reviews for [Frozen 2] fashion collaboration products-Semantic networks and sentiment analysis. The Research Journal of the Costume Culture, 28(2), 265-284. doi:10.29049/rjcc.2020.28.2.265
- Chun, C. H. (2012). Data mining techniques. Seoul: Hannarae.
- Chung, E.-S., & Kim, J.-S. (2001). A study on the wearing trend of the women's wear consumer. Journal of Fashion Design, 1(1), 105-126.
- Dong, M., Zeng, X., Koehl, L., & Zhang, J. (2020). An interactive knowledge-based recommender system for fashion product design in the big data environment. Information Sciences, 540, 469-488. doi:10.1016/j.ins.2020.05.094
- Donnellan, J., McDonald, M., & Edmondson, M. (2020). Impact of social media on consumer buying patterns. International Journal of Marketing Studies, 12(3), 71-79. doi:10.5539/ijms.v12n3p71
- DuBreuil, M., & Lu, S. (2020). Traditional vs. big-data fashion trend forecasting: An examination using WGSN and EDITED. International Journal of Fashion Design, Technology and Education, 13(1), 68-77. doi:10.1080/17543266.2020.1732482
- Easey, M. (2009). Fashion marketing. New Jersey: John Wiley & Sons.
- EMC Education Services. (2015). Data science and big data analytics: Discovering, analyzing, visualizing and presenting data. New Jersey: Wiley.
- Fill, C., & Turnbull, S. L. (2016). Marketing communications: Brands, experiences and participation. London: Pearson.
- Fletcher, K. (2003). Consumer power and privacy: The changing nature of CRM. International Journal of Advertising, 22(2), 249-272. doi:10.1080/02650487.2003.11072851
- Hand, D., Heikki, M., & Padhraic, S. (2001). Principles of data mining (adaptive computation and machine learning). Massachusetts: MIT Press.
- Heckhausen, J., Dixon, R. A., & Baltes, P. B. (1989). Gains and losses in development throughout adulthood as perceived by different adult age groups. Developmental Psychology, 25(1), 109-121. doi:10.1037/0012-1649.25.1.109
- Henderson, P. W., & Cote, J. A. (1998). Guidelines for selecting or modifying logos. Journal of Marketing, 62(2), 14-30. doi:10.1177/002224299806200202
- Hoyer, W. D., MacInnis, D. J., & Pieters, R. (2012). Consumer behavior. Boston: Cengage Learning.
- Huh, J., & Lee, E.-J. (2019). An exploratory analysis of the web-based keywords of fashion brands using big-data: Focusing on their links to the brand's key marketing strategies. The Research Journal of the Costume Culture, 27(4), 398-413. doi:10.29049/rjcc.2019.27.4.398
- Hwangbo, H., Kim, Y. S., & Cha, K. J. (2018). Recommendation system development for fashion retail e-commerce. Electronic Commerce Research and Applications, 28, 94-101. doi:10.1016/j.elerap.2018.01.012
- Jung, Y. G., Park, J. K., Lee, J. C., & Choi, E. Y. (2012). An study on the product purchase patterns using association rule. Journal of Service Research and Studies, 2(1), 39-46.
- Kardes, F., Cronley, M., & Cline, T. (2014). Consumer behavior. Boston: Cengage Learning.
- Kim, D. J. (2022, March 15). '패션산업의 풍향을 바꾼다'...AI 기반 개인 맞춤형 패션 스타트업들의 이유있는 '고공비행' ['Changing the direction of the fashion industry'...AI-based personalized fashion startups reasonable 'flight high']. The Stock. Retrieved October 25, 2022, from https://www.the-stock.kr/news/articleView.html?idxno=15928
- Kim, J. H., & Lee, Y. J. (2010). A study on clothing purchasing behavior and preference images of the fashion items according to the age groups of the elderly woman. The Korean Fashion and Textiles Research Journal, 12(3), 279-290. doi: 10.5805/KSCI.2010.12.3.279
- Kim, S., Jeong, J., & Lee, Y. (2020). Effects of social identification on consumers' attitude and purchase intention for university logo products: Focusing on moderating effects of university prestige and online shopping frequency. The Research Journal of the Costume Culture, 28(6), 755-770. doi:10.9049/rjcc.2020.28.6.755
- Korean Federation of Textiles Industries. (2022a, November 16). Market trend 2022. Fashionnet. Retrieved March 5, 2023, from https://www.fashionnet.or.kr/research-report/101367/
- Korean Federation of Textiles Industries. (2022b, March 6). Special report golf. Fashionnet. Retrieved March 5, 2023, from https://www.fashionnet.or.kr/market-retail-trends/64582/
- Kudyba, S. (2014). Big data, mining, and analytics: Components of strategic decision making. Florida: CRC Press.
- Li, X., & Law, R. (2020). Network analysis of big data research in tourism. Tourism Management Perspectives, 33, 1-12. doi:10.1016/j.tmp.2019.100608
- Linoff, G. S., & Berry, M. J. (2011). Data mining techniques: For marketing, sales, and customer relationship management (3th ed.). New Jersey: John Wiley & Sons.
- Lumpkin, J. R. (1984). The effect of retirement versus age on the shopping orientations of the older consumer. The Gerontologist, 24(6), 622-627. doi:10.1093/geront/24.6.622
- Lury, C. (1996). Consumer culture. New Jersey: Rutgers University Press.
- McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60-68.
- Moe, W. W., & Fader, P. S. (2004). Capturing evolving visit behavior in clickstream data. Journal of Interactive Marketing, 18(1), 5-19. doi:10.1002/dir.10074
- Moller Jensen, J., & Hansen, T. (2006). An empirical examination of brand loyalty. Journal of Product & Brand Management, 15(7), 442-449. doi:10.1108/10610420610712829
- Nakahara, T., & Yada, K. (2012). Analyzing consumers' shopping behavior using RFID data and pattern mining. Advances in Data Analysis and Classification, 6, 355-365 https://doi.org/10.1007/s11634-012-0117-z
- Nam, K. (2022). Conversion paths of online consumers: A sequential pattern mining approach. Expert Systems with Applications, 202, 117253. doi:10.1016/j.eswa.2022.117253
- Parise, S., Iyer, B., & Vesset, D. (2012). Four strategies to capture and create value from big data. Ivey Business Journal, 76(4), 1-5.
- Passyn, K. A., Diriker, M., & Settle, R. B. (2011). Images of online versus store shopping: Have the attitudes of men and women, young and old really changed? Journal of Business & Economics Research, 9(1), 99-110. doi:10.19030/jber.v9i1.946
- Rahman, O., Fung, B. C. M., & Liu, W. (2014). Using data mining to analyse fashion consumers' preferences from a cross-national perspective. International Journal of Fashion Design, Technology and Education, 7(1), 42-49. doi:10.1080/17543266.2013.864340
- Saravana Kumar, N. M., Eswari, T., Sampath, P., & Lavanya, S. (2015). Predictive methodology for diabetic data analysis in big data. Procedia Computer Science, 50, 203-208. doi:10.1016/j.procs.2015.04.069
- Seo, J. P. (2020, January 29). 패션기업 빅데이터 활용, 선택이 아닌 필수! [Utilizing big data for fashion companies, a must, not a choice!]. Fashion Insight. Retrieved October 25, 2020, from http://www.fi.co.kr/mobile/view.asp?idx=68281
- Shin, S. (2001). A study on the shopping orientation, Importance of store attributes, and self-image according to the clothing benefits of the adult males of twenties and thirties. The Research Journal of the Costume Culture, 9(6), 818-829.
- Shin, S.-Y., & Kim, Y.-I. (2013). The classification of fashion frame and fashion image of Korean women in their twenties and thirties. Journal of the Korean Society of Costume, 63(4), 118-131. doi:10.7233/jksc.2013.63.4.118
- Singh, P., Katiyar, N., & Verma, G. (2014). Retail shoppability: The impact of store atmospherics & store layout on consumer buying patterns. International Journal of Scientific & Technology Research, 3(8), 15-23.
- Sorescu, A. (2017). Data-driven business model innovation. Journal of Product Innovation Management, 34(5), 691-696. doi:10.1111/jpim.12398
- Yoshimura, Y., Sobolevsky, S., Bautista Hobin, J. N., Ratti, C., & Blat, J. (2018). Urban association rules: Uncovering linked trips for shopping behavior. Environment and Planning B: Urban Analytics and City Science, 45(2), 367-385. doi:10.1177/02658135166764
- Zaki, M. J. (2001). SPADE: An efficient algorithm for mining frequent sequences. Machine Learning, 42, 31-60. doi:10.1023/A:1007652502315
- Zhang, Y. (2015). The impact of brand image on consumer behavior: A literature review. Open Journal of Business and Management, 3(1), 58-62. doi:10.4236/ojbm.2015.31006