Temporal analysis of clusters of supermarket customers: conventional versus interval set approach

  • Authors:

  • Pawan Lingras;Mofreh Hogo;Miroslav Snorek;Chad West

  • Affiliations:

  • Department of Mathematics and Computer Science, Saint Mary's University, Halifax, NS, Canada B3H 3C3;Department of Computer Science and Engineering, Faculty of Electrical Engineering, Czech Technical University, Karlovo Nam. 13, 121 35 Prague 2, Czech Republic;Department of Computer Science and Engineering, Faculty of Electrical Engineering, Czech Technical University, Karlovo Nam. 13, 121 35 Prague 2, Czech Republic;IBM Toronto Software Development Laboratory, 8200 Warden Ave, Markham, ON, Canada L6G 1C7

  • Venue:
  • Information Sciences—Informatics and Computer Science: An International Journal
  • Year:
  • 2005

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Abstract

Temporal data mining is the application of data mining techniques to data that takes the time dimension into account. This paper studies changes in cluster characteristics of supermarket customers over a 24 week period. Such an analysis can be useful for formulating marketing strategies. Marketing managers may want to focus on specific groups of customers. Therefore they may need to understand the migrations of the customers from one group to another group. The marketing strategies may depend on the desirability of these cluster migrations. The temporal analysis presented here is based on conventional and modified Kohonen self organizing maps (SOM). The modified Kohonen SOM creates interval set representations of clusters using properties of rough sets. A description of an experimental design for temporal cluster migration studies including, data cleaning, data abstraction, data segmentation, and data sorting, is provided. The paper compares conventional and non-conventional (interval set) clustering techniques, as well as temporal and non-temporal analysis of customer loyalty. The interval set clustering is shown to provide an interesting dimension to such a temporal analysis.