JSM 2004 - Toronto

Abstract #300987

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Activity Number: 325
Type: Topic Contributed
Date/Time: Wednesday, August 11, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and Marketing
Abstract - #300987
Title: Effect of Simpson's Paradox on Market Basket Analysis
Author(s): Yuejiao Ma*+ and Dennis K.J. Lin
Companies: Pennsylvania State University and Pennsylvania State University
Address: 326 Thomas Bldg., University Park, PA, 16802,
Keywords: Market Basket Analysis ; association rule ; confidence ; improvement ; Simpson's Paradox ; common improvement
Abstract:

One of the well-studied problems in data-mining is pruning for association rules in Market Basket Analysis. Association rules discovery is an important database-mining algorithm that finds interesting association or correlation relationships among a set of items. Association rule induction is a powerful method for Market Basket Analysis, which aims at finding regularities in the shopping behavior of supermarket customers, online shops and the like. A useful association rule usually satisfies three measurements: support, confidence, and improvement, with minimum support, minimum confidence, and improvement greater than one. The measurements of Association rules are based on the aggregated dataset. It is very easy for the decision-maker to misinterpret the real relationship reflected in a nonaggregated dataset and miss potentially useful association rules. This well-known phenomenon is due to Simpson's Paradox that was defined by Simpson in 1951. For example, based upon the aggregated dataset, we find that the rule {if a customer buys product A, then this customer will also buy product B} does not satisfy the requirements at all.

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