Application of uninorms to market basket analysis
The ability for grocery retailers to have a single view of customers across all their grocery purchases remains elusive and has become increasingly important in recent years (especially in the UK) where competition has intensified, shopping habits and demographics have changed and price sensitivity has increased following the 2008 recession. Numerous studies have been conducted on understanding independent items that are frequently bought together (association rule mining/ frequent itemsets) with several measures proposed to aggregate item support and rule confidence with varying levels of accuracy as these measures are highly context dependent. Uninorms were used as an alternative measure to aggregate support and confidence in analysing market basket data using the UK grocery retail sector as a case study. Experiments were conducted on consumer panel data with the aim of comparing the uninorm against three other popular measures (Jaccard, Cosine and Conviction). It was found that the uninorm outperformed other models on its adherence to the fundamental monotonicity property of support in market basket analysis. Future work will include the extension of this analysis to provide a generalised model for market basket analysis.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Uninorms, Frequent itemset mining, Market basket analysis, aggregate measures, support, confidence
Moodley, R., Chiclana, F., Caraffini, F. and Carter, J. (2018) Application of uninorms to market basket analysis. International Journal of Intelligent Systems. 34 (1), pp. 39-49
Institute of Artificial Intelligence (IAI)