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Section: New Results

Keywords : unsupervised clustering, distances table, dynamic clustering algorithm.

Adaptive Distances in Clustering Methods

Participants : Marc Csernel, F.A.T. de Carvalho, Yves Lechevallier.

The adaptive dynamic clustering algorithm [84] and [52] optimizes a criterion based on a fitting measure between clusters and their prototypes, but the distances used to compare clusters and their prototypes change at each iteration. These distances are not determined absolutely and can be different from one cluster to another. The advantage of these adaptive distances is that the clustering algorithm is able to recognize clusters of different shapes and sizes. The main difference between these algorithms lies in the representation step, which has two stages in the adaptive case. The first stage, where the partition and the distances are fixed and the prototypes are updated, is followed by a second one, where the partition and their corresponding prototypes are fixed and the distances are updated.


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