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

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 represe...

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Veröffentlicht in:	IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 2009-11, Vol.39 (6), p.1295-1306
Hauptverfasser:	de A.T. de Carvalho, F., Lechevallier, Y.
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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