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Unsupervised pattern recognition methods for interval data using non-quadratic distances

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2 Author(s)
de Carvalho, F.A.T. ; Center of Informatics, Univ. Fed. de Pernambuco, Recife, Brazil ; de Souza, R.M.C.R.

Unsupervised pattern recognition methods for interval data using a dynamic cluster algorithm are presented. Two methods are considered: one with adaptive distances and the other without. They can be applied to image segmentation. The clustering outputs are compared using an external index. The best results are furnished by adaptive methods.

Published in:

Electronics Letters  (Volume:39 ,  Issue: 5 )