Unsupervised learning through symbolic clustering

Authors:
K. Chidananda Gowda;E. Diday
Affiliations:
-;-
Venue:
Pattern Recognition Letters
Year:
1991

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Abstract

A symbolic clustering method using a new similarity measure, based on 'position', 'span', and 'content' of symbolic objects, is presented for the unsupervised learning of the mean vectors of the components of a mixture of multivariate normal densities, when the number of classes is also unknown.