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Dynamical clustering of interval data: Optimization of an adequacy criterion based on Hausdorff distance. (English) Zbl 1032.62058

Jajuga, Krzysztof et al., Classification, clustering, and data analysis. Recent advances and applications. Papers presented at the eighth conference of the International Federation of Classification Societies (IFCS), Cracow, Poland, July 16-19, 2002. Berlin: Springer. Studies in Classification, Data Analysis, and Knowledge Organization. 53-60 (2002).
Summary: In order to extend the dynamical clustering algorithm to interval data sets, we define the prototype of a cluster by optimization of a classical adequacy criterion based on Hausdorff distance. Once this class prototype is properly defined we give a simple and converging algorithm for this new type of interval data.
For the entire collection see [Zbl 1026.00018].

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
65C60 Computational problems in statistics (MSC2010)
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