Summary
This paper introduces a partitioning clustering method for objects described by interval data. It follows the dynamic clustering approach and uses and L 2 distance. Particular emphasis is put on the standardization problem where we propose and investigate three standardization techniques for interval-type variables. Moreover, various tools for cluster interpretation are presented and illustrated by simulated and real-case data.
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Acknowledgements
The first author would like to thank CNPq (Brazilian Agency) for its financial support. The second author would like to thank Calouste Gulbenkian Foundation and FCT/MCTES (Portuguese Agency).
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de Carvalho, F.d.A.T., Brito, P. & Bock, HH. Dynamic clustering for interval data based on L 2 distance. Computational Statistics 21, 231–250 (2006). https://doi.org/10.1007/s00180-006-0261-z
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DOI: https://doi.org/10.1007/s00180-006-0261-z