Relative Linkage Disequilibrium Applications to Aircraft Accidents and Operational Risks

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 lift. Relative Linkage Disequilibrium (RLD) was proposed in as an approach to analyse both quantitatively and graphically association rules 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. Moreover RLD can be interpreted graphically using a simplex representation leading to powerful graphical display of association relationships. In this paper we demonstrate the strength of RLD by applying it to two large data sets. One data set consists of 2008 aircraft accident and incident occurrences recorded in the FAA data base. The other data set consists of operational risks captured by a large financial institution operating under Basel II regulations.