Abstract
The task of fuzzy clustering data is very interesting and important problem and often found in many applications related to data mining and exploratory data analysis. For solving these problems the traditional methods require that every vector-observations are fed from data could belong to only one cluster. A more natural is situation when a vector-observations with the various possibilities of membership levels can belong more, than one cluster. In this situation more effective are methods of fuzzy clustering that are synthesized for the allowance of the mutual overlapping of the classes, which are formed in the process of analyzing the data.
Novadays, the most widespread algorithms of probabilistic fuzzy clustering. At the same time, this approach has the significant disadvantages associated with strict “probabilistic” constraints on the level of membership and increased sensitivity to abnormal observation, which are often present in the initial data sets.
Therefore, as an alternative to probabilistic fuzzy clustering methods the recurrent modification credibilistic fuzzy clustering method, was proposed that’s based on credibility approach and Gustafson - Kessel algorithm for fuzzy clustering.
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Bodyanskiy, Y., Shafronenko, A., Klymova, I., Polyvoda, V. (2022). Robust Recurrent Credibilistic Modification of the Gustafson - Kessel Algorithm. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_42
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