Association rule mining is one technique that is widely used to obtain useful associations rules among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, there is a lack of interestingness measures developed for multi-level and cross-level Association rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach, which does not consider for the hierarchy. In this paper we propose two approaches which measure multi-level association rules to help and evaluate their interestingness. These measures of diversity and peculiarity can be used to identify those rules from multi-level datasets that are potentially useful.
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