Partitional clustering algorithms for symbolic interval data based on single adaptive distances

This paper introduces dynamic clustering methods for partitioning symbolic interval data. These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between clusters and their representatives. To compare symbolic interval data, these methods use single adaptive (city-block and Hausdorff) distances that change at each iteration, but are the same for all clusters. Moreover, various tools for the partition and cluster interpretation of symbolic interval data furnished by these algorithms are also presented. Experiments with real and synthetic symbolic interval data sets demonstrate the usefulness of these adaptive clustering methods and the merit of the partition and cluster interpretation tools.