Abstract
In this paper a robust fuzzy methodology for simultaneously clustering objects and variables is proposed. Starting from Double k-Means, different fuzzy generalizations for categorical multivariate data have been proposed in literature which are not appropriate for heterogeneous two-mode datasets, especially if outliers occur. In practice, in these cases, the existing fuzzy procedures do not recognize them. In order to overcome that inconvenience and to take into account a certain amount of outlying observations a new fuzzy approach with noise clusters for the objects and variables is introduced and discussed.
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© 2015 Springer International Publishing Switzerland
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Ferraro, M.B., Vichi, M. (2015). Fuzzy Double Clustering: A Robust Proposal. In: Grzegorzewski, P., Gagolewski, M., Hryniewicz, O., Gil, M. (eds) Strengthening Links Between Data Analysis and Soft Computing. Advances in Intelligent Systems and Computing, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-319-10765-3_27
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DOI: https://doi.org/10.1007/978-3-319-10765-3_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10764-6
Online ISBN: 978-3-319-10765-3
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