Clustering of symbolic data through a dissimilarity volume based measure
Kelly P. Silva, Francisco A. T. de Carvalho, Marc Csernel · 2008
The recording of symbolic data has become a common practice with the advances in database technologies. This paper shows hard and fuzzy relational clustering in order to partition symbolic data. These methods optimize objective functions based on a dissimilarity function. The distance used is a volume based measure and may be applied to data described by set-valued, list-valued or interval-valued symbolic variables. Experiments with real and synthetic symbolic data sets show the usefulness of the proposed approach.