Abstract
Fuzzy prototypes characterise data categories underlining both the common features of the category members and their discriminative features as opposed to other categories. In this paper, a clustering algorithm based on these principles is presented. It offers means to handle outliers, and a cluster repulsion effect avoiding overlapping areas between clusters. Moreover, it makes it possible to characterise the obtained clusters with prototypes, increasing the result interpretability.
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Lesot, MJ., Kruse, R. (2007). Typicality Degrees and Fuzzy Prototypes for Clustering. In: Decker, R., Lenz, H.J. (eds) Advances in Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70981-7_13
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DOI: https://doi.org/10.1007/978-3-540-70981-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70980-0
Online ISBN: 978-3-540-70981-7
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