Data summarisation by typicality-based clustering for vectorial data and nonvectorial data
Marie-Jeanne Lesot and Rudolf Kruse
In: IEEE Conference on fuzzy systems, vancouver, Canada(2006).

Abstract

In this paper, a typicality-based clustering algorithm is proposed: it exploits typicality degrees defined in a prototype construction framework to identify a decomposition of the dataset into homogeneous and distinct clusters and to provide characteristic representatives of the obtained clusters, so as to summarise the initial dataset. The proposed algorithm can be applied both to vectorial and non vectorial data, such as trees for instance. Tests performed on artificial and real data illustrate the interest of the proposed approach.

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