Clustering of symbolic interval data based on a single adaptive L1 distance

The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper introduces a dynamic clustering method to partitioning symbolic interval data. This method furnishes a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. To compare symbolic interval data, the method uses a single adaptive L₁ distance that at each iteration changes but is the same for all the clusters. Experiments with real and synthetic symbolic interval data sets showed the usefulness of the proposed method.