Communication Dans Un Congrès Année : 2014

Diversity analysis in collaborative clustering

Résumé

The aim of collaborative clustering is to reveal the common structure of data which are distributed on different sites. The topological collaborative clustering, based on SelfOrganizing Maps (SOM) is an unsupervised learning method which is able to use the output of other SOMs from other sites during the learning. This paper investigates the impact of the diversity between collaborators on the collaboration’s quality and presents a study of different diversity indexes for collaborative clustering. Based on experiments on artificial and real datasets, we demonstrated that the quality and the diversity of the collaboration can have an important impact on the quality of the collaboration and that not all diversity indexes are relevant for this task.

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hal-05001290 , version 1 (22-03-2025)

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Nistor Grozavu, Guénaël Cabanes, Younès Bennani. Diversity analysis in collaborative clustering. 2014 International Joint Conference on Neural Networks (IJCNN), Jul 2014, Beijing, China. pp.1754-1761, ⟨10.1109/IJCNN.2014.6889528⟩. ⟨hal-05001290⟩
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