Browse > Article
http://dx.doi.org/10.7232/IEIF.2011.24.4.447

A New Approach to Spatial Pattern Clustering based on Longest Common Subsequence with application to a Grocery  

Jung, In-Chul (Department of Industrial and Systems Engineering, Dongguk University)
Kwon, Young-S. (Department of Industrial and Systems Engineering, Dongguk University)
Publication Information
IE interfaces / v.24, no.4, 2011 , pp. 447-456 More about this Journal
Abstract
Identifying the major moving patterns of shoppers' movements in the selling floor has been a longstanding issue in the retailing industry. With the advent of RFID technology, it has been easier to collect the moving data for a individual shopper's movement. Most of the previous studies used the traditional clustering technique to identify the major moving pattern of customers. However, in using clustering technique, due to the spatial constraint (aisle layout or other physical obstructions in the store), standard clustering methods are not feasible for moving data like shopping path should be adjusted for the analysis in advance, which is time-consuming and causes data distortion. To alleviate this problems, we propose a new approach to spatial pattern clustering based on longest common subsequence (LCSS). Experimental results using the real data obtained from a grocery in Seoul show that the proposed method performs well in finding the hot spot and dead spot as well as in finding the major path patterns of customer movements.
Keywords
customer path; shopping behavior; exploratory analysis; LCSS; RFID;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Larson, J. S., Bradlow, E. T., and Fader, P. S. (2005), An exploratory look at supermarket shopping paths, International Journal of Research in Marketing, 22(4), 395-414.   DOI   ScienceOn
2 McClure, P. J. and West, E. J. (1969), Sales Effects of a New Counter Display, Journal of Advertising Research, 9, 29-34.
3 Newman, A. J., Yu, D. K. C., and Oulton , D. P. (2002), New insights into retail space and format planning from customer-tracking data, Journal of Retailing and Consumer Services, 9(5), 253-258.   DOI   ScienceOn
4 Uotila, V. and Skogster, P. (2007), Space management in a DIY store analysing consumer shopping paths with data-tracking devices, Facilities, 25(9), 363- 374.   DOI
5 Yanagisawa, Y., Akahani, J.-I., and Satoh, T. (2003), Shape-based Similarity Query for Trajectory of Mobile Objects, Proceedings of MDM, 63-77.
6 Cao, H., Mamoulis, N., and Cheung, D. W. (2007), Discovery of Periodic Patterns in Spatiotemporal Sequences, Knowledge and Data Engineering, IEEE Transactions on, 19(4), 453-467.
7 Cox, K. (1964), The Responsiveness of Food Sales to Shelf Space Changes in Supermarkets, Journal of Marketing Research, 1(2), 63-67.   DOI   ScienceOn
8 Dickson, P. R. and Sawyer, A. G. (1986), Point-of-Purchase Behavior and Price Perceptions of Supermarket Shoppers, Working Paper, 86-102, Marketing Science Institute, 1000 Massachusetts Ave., Cambridge, MA 02138.
9 Farley, J. U. and Ring, L. W. (1996), A Stochastic Model of Supermarket Traffic Flow, OPERATIONS RESEARCH, 14(4), 555-567.
10 Gil, J., Tobari, E., Lemlij, M., Rose, A., and Penn, A. (2009), The Differentiating Behaviour of Shoppers : Clustering of Individual Movement Traces in a Supermarket, Proceedings of the 7th International Space Syntax Symposium
11 Harris, D. H. (1958), The effect of display width in merchandising soap, Journal of Applied Psychology, 42(4), 283-284.   DOI
12 Hirschberg, D. S. (1977), Algorithms for the longest common subsequence problem, Journal of ACM, 24(4), 664-675.   DOI
13 Hou, J.-L. and Chen, T.-G. (2011), An RFID-based Shopping Service System for retailers, Advanced Engineering Informatics, 25(1), 103-115.   DOI   ScienceOn
14 Hoyer, W. D. (1984), An Examination of Consumer Decision Making for a Common Repeat Purchase Product, Journal of Consumer Research, 11(3), 822- 829.   DOI   ScienceOn
15 Hui, S. K., Bradlow, E. T., and Fader, P. S. (2009), Testing Behavioral Hypotheses Using an Integrated Model of Grocery Store Shopping path and purchase Behavior, Journal of consumer research, 36, 478-493.   DOI   ScienceOn
16 Kim, Y.-C. and Chang, J.-W. (2008), Grid-based Similar Trajectory Search for Moving Objects on Road Network, Journal of Korea Spatial Information System Society, 10(1), 29-40.
17 Hui, S. K., Fader, P. S., and Bradlow, E. T. (2009), Path Data in Marketing : An Integrative Framework and Prospectus for Model Building, Marketing Science, 28(2), 320-335.   DOI   ScienceOn
18 Kang, H.-Y., Kim, J.-S., Hwang, J.-R., and Li K.-J. (2008), Similarity measures for trajectories of moving objects in cellular space, GIS Association of Korea, 16(4), 291-301.