DOI QR코드

DOI QR Code

Analysis of Market Trajectory Data using k-NN

  • Park, So-Hyun (Department of IT Engineering, Sookmyung Women's University) ;
  • Ihm, Sun-Young (Department of IT Engineering, Sookmyung Women's University) ;
  • Park, Young-Ho (Department of IT Engineering, Sookmyung Women's University)
  • 투고 : 2018.07.11
  • 심사 : 2018.08.13
  • 발행 : 2018.09.30

초록

Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.

키워드

참고문헌

  1. J. S. Hwang, S. Y. Pi, C. S. Son, H. M. Chung, "A Purchase Pattern Analysis Using Bayesian Network and Neural Network", International Journal of Fuzzy Logic and Intelligent systems, vol. 15, no. 3, pp. 306-311, 2005
  2. J. W. Kim, W. S. Lee, "Purchase Prediction and Marketing Utilization Through Pseudo Periodic Pattern Analysis", in Proceedings of Korean Institute of Information Technology Summer Conference, pp. 52- 55, June. 2017.
  3. Y. S. Cho, S. C. Moon, K. H. Ryu, "SOM Clustering Method based on RFM Analysis for Predicting Customer Purchase Pattern in u-Commerce", Journal of The Korea Society of Computer and Information, vol. 21, no. 2, pp. 185-187, July. 2013.
  4. N. Y. Kang, J. Y. Kang, H. S. Yong, "Performance Comparison of Clustering Techniques for Spatio- Temporal Data", Journal of Korea Intelligent Information System Society, vol. 10, no. 2, pp. 15-37, Nov. 2004.
  5. J. H. Hong, K. S. Park, Y. K. Han, Y. K. Lee "A Method for Measuring Similarity between Trajectory Graph Sets", Journal of Korea Intelligent Information System Society, vol. 40, no. 3, pp. 153-158, 2013.
  6. M. Y. Jang, M. Yoon, J. W. Chang, "A Survey on Moving Object Trajectory Mining Techniques in Location-based Services", Journal of Korea Intelligent Information System Society, vol. 28. No. 1, pp. 67-68, 2012.
  7. X. Xu, J. Zhou, Y. Liu, Z. Xu, X. Zhao, "Taxi-RS: Taxi Recommendation System Based on Taxi GPS Data", IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 4, pp. 1716-1727, 2015. https://doi.org/10.1109/TITS.2014.2371815
  8. W. Yang, X. Wang, S. M. Rahimi, J. Luo, "Recommending Profitable Taxi Travel Routes Based on Big Taxi Trajectories Data", in Proceeding of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015), pp. 370-382, 2015.