Hierarchical and Pyramidal Clustering for Symbolic Data

This paper presents a method for clustering a set of symbolic data where individuals are described by symbolic variables of various types: interval, categorical multi-valued or modal variables, which take into account the variability or uncertainty present in the data. Hierarchical and pyramidal clustering models are considered. The constructed clusters correspond to concepts, that is, they are maximal sets of individuals associated with a conjunction of properties relating to the variables such that they form necessary and sufficient conditions for cluster membership. More generally, the data may include hierarchical rules between variables as well.