Simple Pre- and Post-Pruning Techniques for Large Conceptual Clustering Structures

In (Godin et al., 1995a) we proposed an incremental conceptual clustering algorithm, derived from lattice theory (Godin et al., 1995b), which is fast to compute (Mineau & Godin, 1995). This algorithm is especially useful when dealing with large data or knowledge bases, making classification structures available to large size applications like those found in industrial settings. However, in order to be applicable on large data sets, the analysis component of the algorithm had to be simplified: the thorough 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 less semantics, or which should not have been formed in the first place. Consequently, some classes 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 simple 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 a pre-pruning technique, while the other two are called postpruning techniques.