Meta clustering starts from different clusterings of the same data and aims to group them, reducing the complexity of the choice of the best partitioning and the number of alternatives to compare. Starting from a collection of single feature clusterings, a graded possibilistic medoid meta clustering algorithm is proposed in this paper, exploiting the soft transition from probabilistic to possibilistic memberships in a way that produces more compact and separated clusters with respect to other medoid-based algorithms. The performance of the algorithm has been evaluated on six publicly available data sets over three medoid-based competitors, yielding promising results.

Graded Possibilistic Meta Clustering

Ferone A.;Maratea A.
2020

Abstract

Meta clustering starts from different clusterings of the same data and aims to group them, reducing the complexity of the choice of the best partitioning and the number of alternatives to compare. Starting from a collection of single feature clusterings, a graded possibilistic medoid meta clustering algorithm is proposed in this paper, exploiting the soft transition from probabilistic to possibilistic memberships in a way that produces more compact and separated clusters with respect to other medoid-based algorithms. The performance of the algorithm has been evaluated on six publicly available data sets over three medoid-based competitors, yielding promising results.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/102278
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