Partitioning Clustering Algorithms for Interval-Valued Data based on City-Block Adaptive Distances

Francisco de Assis Tenório CarvalhoIves Lechevallier

The recording of interval-valued data has become a common practice nowadays. This paper presents some partitioning clustering algorithms for interval-valued data. The proposed methods furnish a partition of the input data and a corresponding prototype (a vector of intervals) for each cluster by optimizing an adequacy criterion which is based on suitable adaptive city-block distances between vectors of intervals. Experiment with a real interval-valued data set shows the usefulness of the proposed method.

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