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Market Basket Analysis of Retail Data: Supervised Learning Approach

  • Gabriel Kronberger
  • Michael Affenzeller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6927)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gabriel Kronberger
    • 1
  • Michael Affenzeller
    • 1
  1. 1.Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and MediaUpper Austria University of Applied SciencesHagenbergAustria

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