Symbolic Clustering of Constrained Probabilistic Data

In previous work (Brito and De Carvalho (1999)) we have considered the presence of dependence rules between variables in the framework of a symbolic clustering method. In another paper Brito (1998) has addressed the problem of clustering probabilistic data. The aim of this paper is to bring together the two issues, that is, to take into account dependence rules on probabilistic data. This is accomplished by introducing new generality measures with an appropriate generalization operator. This approach allows for the extension of a symbolic clustering method to constrained probabilistic data.