Partitioning fuzzy clustering algorithms for mixed feature-type symbolic data

This paper presents partitioning fuzzy clustering algorithms for mixed feature-type symbolic data. The proposed algorithms need a previous pre-processing step in order to obtain a suitable homogenization of the mixed feature-type symbolic data into histogram-valued symbolic data. These fuzzy clustering algorithms give a fuzzy partition and a prototype for each fuzzy cluster by optimizing an adequacy criterion based on suitable adaptive and non-adaptive Euclidean distances between vectors of histogram-valued data. The adaptive Euclidean distances change at each algorithm iteration and are different from one fuzzy cluster to another. Experiments with real mixed feature-type symbolic data sets show the usefulness of these fuzzy clustering algorithms.