Unsupervised collaborative boosting of clustering: an unifying framework for multi-view clustering, multiple consensus clusterings and alternative clustering

Jacques-Henri Sublemontier 1
1 CA
LIFO - Laboratoire d'Informatique Fondamentale d'Orléans
Abstract : In this paper, we propose a collaborative framework that is able to solve multi-view and alternative clustering problems using some clustering ensemble and semi-supervised clustering principles. We provide a mechanism to control, via a information sharing model, different clustering algorithms to obtain consensus or alternative clustering solutions. The strong point is that our approach does not need to know which clustering algorithms to use nor their parameters
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Communication dans un congrès
International Joint Conference on Neural Networks (IJCNN 2013), Aug 2013, Dallas, United States. 2013
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https://hal.inria.fr/hal-00813201
Contributeur : Jacques-Henri Sublemontier <>
Soumis le : lundi 15 avril 2013 - 11:34:25
Dernière modification le : vendredi 13 juillet 2018 - 11:44:06

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  • HAL Id : hal-00813201, version 1

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Jacques-Henri Sublemontier. Unsupervised collaborative boosting of clustering: an unifying framework for multi-view clustering, multiple consensus clusterings and alternative clustering. International Joint Conference on Neural Networks (IJCNN 2013), Aug 2013, Dallas, United States. 2013. 〈hal-00813201〉

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