Clustering interval-valued data with automatic variables weighting
Sara Inés Rizo Rodríguez, Francisco de A.T. de Carvalho · 2019
Over the past few years, Symbolic Data Analysis has gained popularity providing suitable methods for managing aggregated data represented by lists, intervals, histograms or even distributions. This paper proposes a partitioning clustering algorithm for interval-valued data based on the suitable adaptive Euclidean distance that takes into account the relevance of the variables according to the boundaries. The proposed distance changes at each algorithm iteration and is different from one cluster to another. The method provides a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between groups and their representatives. Experiments on synthetic and real interval-valued datasets corroborate the usefulness of the proposed algorithm.