Association rules are one of the most popular unsupervised data mining methods. Once obtained, the list of association rules extractable from a given dataset is compared in order to evaluate their importance level. The measures commonly used to assess the strength of an association rule are the indexes of support, confidence, and the lift.
Relative Linkage Disequilibrium (RLD) was originally proposed as an approach to analyse both quantitatively and graphically general two way contingency tables. RLD can be considered an adaptation of the lift measure with the advantage that it presents more effectively the deviation of the support of the whole rule from the support expected under independence given the supports of the LHS (A) and the RHS (B). RLD can be interpreted graphically using a simplex representation leading to powerful graphical display of association relationships. Moreover the statistical properties of RLD are known so that confirmatory statistical tests of significance or basic confidence intervals can be applied.
This paper will present the properties of RLD in the context of association rules and provide several application examples to demonstrate it’s practical advantages.