Market basket analysis of retail data: supervised learning approach

Authors:
Gabriel Kronberger;Michael Affenzeller
Affiliations:
Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media, Upper Austria University of Applied Sciences, Hagenberg, Austria;Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media, Upper Austria University of Applied Sciences, Hagenberg, Austria
Venue:
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Year:
2011

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Abstract

In this work we discuss a supervised learning approach for identification of frequent itemsets and association rules from transactional data. This task is typically encountered in market basket analysis, where the goal is to find subsets of products that are frequently purchased in combination. In this work we compare the traditional approach and the supervised learning approach to find association rules in a real-world retail data set using two well known algorithm, namely Apriori and PRIM.