A self-tuning possibilistic c-means clustering algorithm

Szilágyi, László and Lefkovits, Szidónia and Kucsván, Zsolt Levente (2018) A self-tuning possibilistic c-means clustering algorithm. In: Modeling Decisions for Artificial Intelligence, 15-18 Oct 2018, Palma de Mallorca.

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Most c-means clustering models have serious difficulties when facing clusters of different sizes and severely outlier data. The possibilistic c-means (PCM) algorithm can handle both problems to some extent. However, its recommended initialization using a terminal partition produced by the probabilistic fuzzy c-means does not work when severe outliers are present. This paper proposes a possibilistic c-means clustering model that uses only three parameters independently of the number of clusters, which is able to more robustly handle the above mentioned obstacles. Numerical evaluation involving synthetic and standard test data sets prove the advantages of the proposed clustering model.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science / természettudomány > QA Mathematics / matematika > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Depositing User: Dr. László Szilágyi
Date Deposited: 16 Sep 2019 06:08
Last Modified: 21 Sep 2019 10:33

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