However, in order to be applicable on large data sets, the analysis component of the algorithm had to be simplified: the thoroguh comparison of objects normally needed to fully justify the formation of classes had to be cut down. Of course, from less analysis results classes which carry les semantics, or which should not have been formed in the first place. Consequently, some classess are useless in terms of the information needs of the applications that will later on interact with the data. Pruning techniques are thus needed to eliminate these classes and simplify the classification structure.

However, since these classification structures are huge, the pruning techniques themselves must be simle so that they can be applied in reasonable time on large classification structures. This paper presents three such techniques: one is based on the definition of constraints over the generalization language, the other two are based on discrimination metrics applied on links between classes or on the classes themselves. Because the first technique is applied before the classification structure is built, it is called the pre-pruning technique, while the other two are called post-pruning techniques.