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.

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

FRANCIA, MATTEO;Matteo Golfarelli;Stefano Rizzi
2018

Abstract

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.
2018
Proceedings 26th Italian Symposium on Advanced Database Systems
1
8
Matteo Francia, Matteo Golfarelli, Stefano Rizzi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/642343
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