Dynamic Clustering of Interval-Valued Data Based on Adaptive Quadratic Distances

• Published in: IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 39; no. 6; pp. 1295 - 1306
• Main Authors: de A.T. de Carvalho, F., Lechevallier, Y.
• Format: Journal Article
• Language: English
• Published: IEEE 01-11-2009
• Subjects: Adaptive quadratic distances; cluster interpretation indexes; Clustering algorithms; clustering analysis; Clustering methods; Data analysis; Data mining; Heuristic algorithms; Iterative algorithms; Optimization methods; partition interpretation indexes; Partitioning algorithms; Pattern recognition; Prototypes; symbolic interval data analysis
• ISSN: 1083-4427; 1558-2426
• DOI: 10.1109/TSMCA.2009.2030167

This paper presents partitioning dynamic clustering methods for interval-valued data based on suitable adaptive quadratic distances. These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. These adaptive quadratic distances change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. Moreover, various tools for the partition and cluster interpretation of interval-valued data are also presented. Experiments with real and synthetic interval-valued data sets show the usefulness of these adaptive clustering methods and the merit of the partition and cluster interpretation tools.