Combining multiple partitions created with a graph-based construction for data clustering

Abstract : This paper focusses on a new clustering method called evidence accumulation clustering with dual rooted prim tree cuts (EAC-DC), based on the principle of cluster ensembles also known as ''combining multiple clustering methods". A simple weak clustering algorithm is introduced based upon the properties of dual rooted minimal spanning trees and it is extended to multiple rooted trees. Co-association measures are proposed that account for the cluster sets obtained by these methods. These are exploited in order to obtain new ensemble consensus clustering algorithms. The EAC-DC methodology applied to both real and synthetic data sets demonstrates the superiority of the proposed methods.
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Communication dans un congrès
IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2009, Sep 2009, Grenoble, France. Proceedings of IEEE International Workshop on Machine Learning for Signal Processing, 2009
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Laurent Galluccio, Olivier Michel, Pierre Comon, Alfred O. Hero, Mark Kliger. Combining multiple partitions created with a graph-based construction for data clustering. IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2009, Sep 2009, Grenoble, France. Proceedings of IEEE International Workshop on Machine Learning for Signal Processing, 2009. 〈hal-00399928〉

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