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The work described in this paper addresses the study of association rules within groups of individuals. The analysis of the characteristics and the behavior of the individuals belonging to such groups in a given database is powerful in practice, since it provides a mechanism to deal with groups rather than isolated individuals. In this paper, we define group association rules and we study interestingness measures for them. These interestingness measures can be used to rank, not only groups of individuals, but also rules within each group. We also compare the rankings provided by those different interestingness measures in order to determine which one provides a better alternative depending on the kind of situations we wish to highlight within large databases with many different and overlapping groups of individuals.