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The Application Method of Machine Learning for Analyzing User Transaction Tendency in Big Data environments
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 Title & Authors
The Application Method of Machine Learning for Analyzing User Transaction Tendency in Big Data environments
Choi, Do-hyeon; Park, Jung-oh;
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Recently in the field of Big Data, there is a trend of collecting and reprocessing the existing data such as products having high interest of customers and past purchase details to be utilized for the analysis of transaction propensity of users(product recommendations, sales forecasts, etc). Studies related to the propensity of previous users has limitations on its range of subjects and investigation timing and difficult to make predictions on detailed products with lack of real-time thus there exists difficult disadvantages of introducing appropriate and quick sales strategy against the trend. This paper utilizes the machine learning algorithm application to analyze the transaction propensity of users. As a result of applying the machine learning algorithm, it has demonstrated that various indicators which can be deduced by detailed product were able to be extracted.
Big data;Machine Learning;Deep Learning;Tendency Analysis;Data Mining;
 Cited by
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한국정보통신학회논문지, 2016. vol.20. 6, pp.1129-1135 crossref(new window)
SNS 데이터 분석을 기반으로 인공지능에 대한 인식 변화 비교 분석,윤유동;양영욱;임희석;

디지털융복합연구, 2016. vol.14. 12, pp.173-182 crossref(new window)
기업 보안을 위한 융합보안 컴플라이언스 관리 모델에 관한 연구,김민수;

융합보안논문지, 2016. vol.16. 5, pp.81-86
The Analysis of the APT Prelude by Big Data Analytics, Journal of the Korea Institute of Information and Communication Engineering, 2016, 20, 6, 1129  crossref(new windwow)
Jo-Hyeon, Park-Sangseon, “Understanding Product Satisfaction in the Context of Online Trading”, Journal of the Korea Contents Association, Vol. 13, No. 5, pp. 436-442, 2013. crossref(new window)

KCA, "Process Mining technology trends for Big Data analysis", Korea Communication Agency, Information Communication Technology Issues & Prospect, 2014.

Choi-Gyeyeong, "Artificial Intelligence: Disruptive innovation and evolution of the Internet platform", Korea Information Society Development Institute, Premium Report, 2015.

Greg Banks, “More growth options up front – Big data enables a new opening step in the growth decisionmaking process”, Deloitte Newsletter, 2014.

Im-Sujong, Min-Okgi, "Machine Learning Technology Trends for Big Data Processing", Electronics and Telecommunications Research Institute, Electronics and Telecommunications Trends, 2012.

Lee-Byoungyup, Lim-Jongtae, Yoo-Jaesoo, “Utilization of Social Media Analysis using Big Data”, The Journal of the Korea Contents Association, Vol. 13, No. 2, pp. 211-219, 2013. crossref(new window)

McCallum, Andrew, and Kamal Nigam, “A comparison of event models for naive bayes text classification”, AAAI-98 workshop on learning for text categorization, 1998.

Winkler, Robert L, "Introduction to Bayesian inference and decision", Vol. 15, No. 4, pp. 938-939, 1973.

BARROS, Rodrigo Coelho, et al, “A Survey of Evolutionary Algorithms for Decision-Tree Induction”, IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, Vol. 42, No. 3, pp. 291-312, 2012.

Choi-Jonghu, et al. “Application of Data Mning Decision Tree”, Statistic Korea, Journal of The Korean Official Statistics, Vol. 4, No. 1, pp. 61-83, 1999.

Altman, Naomi S. “An introduction to kernel and nearest-neighbor nonparametric regression”, The American Statistician, Vol. 46, No. 3, pp. 175-185, 1992.

Hastie, Trevor, and Rolbert Tibshirani. “Discriminant adaptive nearest neighbor classification”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 18, No. 6, pp. 607-616, 1996. crossref(new window)

Agrawal, Rakesh, and Ramakrishnan Srikant. "Fast algorithms for mining association rules", Proc. 20th int. conf. very large data bases, VLDB, Vol. 1215, pp. 487-499, 1994.

Perego, Raffaele, Salvatore Orlando, and P. Palmerini. “Enhancing the apriori algorithm for frequent set counting”, Data Warehousing and Knowledge Discovery, Springer Berlin Heidelberg, pp. 71-82, 2001.