Comparing Reliability of Association Rules and OLAP Statistical Tests

Abstract

Association rules is a technique that can detect patterns within the items of a dataset. The constrained version applies several restrictions that reduces the number of rules and also helps improve performance. On the other hand, OLAP statistical tests is an integration of exploratory On-Line Analytical Processing techniques and statistical tests. It uses a different approach that make it more appropriate for continuous domains and is able to discover more informative patterns. In this article, we thoroughly compare the reliability of the results returned by both techniques by analyzing the metrics, such as confidence and p-value, by which these techniques are implemented in relation to the results that are generated. While these two techniques are different, we were able to bring both to level ground by extending association rules with pairing to discover more specific patterns and extending OLAP statistical tests with constraints to reduce the number of discovered patterns. We conducted our experiments on a real medical dataset and found that the extended OLAP statistical tests discovered more patterns, had comparable performance, and possessed higher reliability due to its strong statistical background.