Uncovering Actionable Knowledge in Corporate Data with Qualified Association Rules

Nenad Jukic, Svetlozar Nestorov, Miguel Velasco and Jami Eddington
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Nenad Jukic: Loyola University Chicago, USA
Svetlozar Nestorov: University of Chicago, USA
Miguel Velasco: University of Minnesota, USA
Jami Eddington: Oklahoma State University, USA

International Journal of Business Intelligence Research (IJBIR), 2011, vol. 2, issue 2, 1-21

Abstract: Association rules mining is one of the most successfully applied data mining methods in today’s business settings (e.g. Amazon or Netflix recommendations to customers). Qualified association rules mining is an extension of the association rules data mining method, that uncovers previously unknown correlations that only manifest themselves under certain circumstances (e.g. on a particular day of the week), with the goal of improving action results, e.g. turning an underperforming campaign (spread too thin over the entire audience) into a highly targeted campaign that delivers results. Such correlations have not been easily reachable using standard data mining tools so far. This paper describes the method for straightforward discovery of qualified association rules and demonstrates the use of qualified association rules mining on an actual corporate data set. The data set is a subset of a corporate data warehouse for Sam’s Club, a division of Wal-Mart Stores, INC. The experiments described in this paper illustrate how qualified association rules supplement standard association rules data mining methods and provide additional information which can be used to better target corporate actions.

Date: 2011
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