A Similarity Function for Multi-Level and Multi-Dimensional Itemsets

The key objective of frequent itemsets (FIs) mining is uncovering relevant patterns from a transactional dataset. In particular we are interested in multi-dimensional and multi-level transactions, i.e., ones that include different points of view about the same event and are described at different levels of detail. In the context of a work aimed at devising original techniques for summarizing and visualizing this kind of itemsets, in this paper we extend the definition of itemset containment to the multi-dimensional and multi-level scenario, and we propose a new similarity function for itemsets, enabling a more effective grouping. The most innovative aspect of our similarity function is that it takes into account both the extensional and intensional natures of itemsets.