Abstract
The term fuzzy clustering usually refers to prototype-based methods that optimize an objective function in order to find a (fuzzy) partition of a given data set and are inspired by the classical c-means clustering algorithm. Possible transfers of other classical approaches, particularly hierarchical agglomerative clustering, received much less attention as starting points for developing fuzzy clustering methods. In this chapter we strive to improve this situation by presenting a (hierarchical) agglomerative fuzzy clustering algorithm. We report experimental results on two well-known data sets on which we compare our method to classical hierarchical agglomerative clustering.
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Borgelt, C., Kruse, R. (2017). Agglomerative Fuzzy Clustering. In: Ferraro, M., et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_9
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DOI: https://doi.org/10.1007/978-3-319-42972-4_9
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