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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Authors would like to acknowledge the financial support for this research through “Bando di sostegno alla ricerca individuale per il triennio 2015–2017 – Annualità 2017” granted by University of Naples “Parthenope”.
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Ferone, A., Maratea, A. (2020). Graded Possibilistic Meta Clustering. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-13-8950-4_18
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DOI: https://doi.org/10.1007/978-981-13-8950-4_18
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