Maximum likelihood function used to calculate confidence of association rules in market baskets
Issue title: Special Supplement Issue in Section A and B: Selected Papers from the ISCA International Conference on Software Engineering and Data Engineering, 2011
Affiliations: Department of Computer Science, North Dakota State University, Fargo, ND, USA | Department of Computer Science, Winona State University, Winona, MN, USA
Correspondence:
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Corresponding author: Arijit Chatterjee, Department of Computer Science, North Dakota State University, Fargo, ND, USA. Tel.: +1 701 540 3804; E-mail: arijit.chatterjee@ndsu.edu.
Abstract: In this paper we are concerned in looking at different ways for calculating the strength of Association Rules in Market Basket data. The significance of Association rules is measured via two measures support and confidence and the way these measures are used to determine strong rules. In the realm of Market Basket Research these measures can be used to find the strength of the rules in a particular transaction of the form, “When a customer buys items A&B also buys item C”. The first portion of this paper illustrates the usage of the method of Maximum Likelihood for Point Estimation and gives an idea how the maximum likelihood estimator can also be used for predicting the confidence of an association rule. The second portion of the paper mainly describes with examples how maximum likelihood function can be used for calculating the collective confidence of association rules.
Keywords: Association rules, maximum likelihood estimator, Market Basket Research
