Abstract
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.
This research was partially supported by the Institute for Research Programs of the Sapientia University. The work of L. Szilágyi was additionally supported by the Hungarian Academy of Sciences through the János Bolyai Fellowship Program. The work of Sz. Lefkovits was additionally supported by UEFISCDI through grant no. PN-III-P2-2.1-BG-2016-0343, contract no. 114BG.
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Szilágyi, L., Lefkovits, S., Kucsván, Z.L. (2018). A Self-tuning Possibilistic c-Means Clustering Algorithm. In: Torra, V., Narukawa, Y., Aguiló, I., González-Hidalgo, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2018. Lecture Notes in Computer Science(), vol 11144. Springer, Cham. https://doi.org/10.1007/978-3-030-00202-2_21
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