A Dynamical Clustering Algorithm for Multi-nominal Data

Abstract

Summary: We present a dynamical clustering algorithm in order to partition a set of multi-nominal data in k classes. This kind of data can be considered as a particular description of symbolic objects. In this algorithm, the representation of the classes is given by prototypes that generalize the characteristics of the elements belonging to each class. A suitable allocation function (context dependent) is considered in this context to assign an object to a class. The final classes are described by the distributions associated to the multi-nominal variables of the elements belonging to each class. That representation corresponds to the usual description of the so-called modal symbolic objects.