Kernel-based fuzzy clustering of interval data

Kernel clustering methods have been very important in application of non-supervised machine learning to real problems. Kernel methods possess many advantages other than non-linearity such as modularity, ability to work with heterogeneous descriptions of data, incorporation of prior knowledge etc. In this paper, we present a clustering method based on kernel functions for partitioning a set of interval-valued data. In addition, this method is compared to a fuzzy partitioning approach for interval data introduced previously. Experiments with real and syntectic symbolic interval-valued data sets are presented. The evaluation of the clustering results furnished by the methods is performed regarding the computation of an external cluster validity index and the global error rate of classification.