Discovery of association rules over ordinal data: A new and faster algorithm and its application to basket analysis

Abstract

This paper argues that quantitative information like prices, amounts bought, and time can give valuable insights into consumer behavior. While Boolean association rules discard any quantitative information, existing algorithms for quantitative association rules can hardly be used for basket analysis. They either lack performance, are restricted to the two-dimensional case, or make questionable assumptions about the data. We propose a new and faster algorithm Q2 for the discovery of multi-dimensional association rules over ordinal data, which is based on ideas presented in [SA96]. Our new algorithm Q2 does not search for quantitative association rules from the very beginning. Instead Q2 prunes out a lot of candidates by first computing the frequent Boolean itemsets. After that, the frequent quantitative itemsets are found in a single pass over the data.

In addition, a new absolute measure for the interestingness of quantitative association rules is introduced. It is based on the view that quantitative association rules have to be interpreted on the background of their Boolean generalizations.

We experimentally compare the new algorithm against the previous approach, obtaining performance improvements of more than an order of magnitude on supermarket data. A rather astonishing result of this paper is that an additional run through the transactions does pay off when searching for quantitative association rules.