Abstract
Fuzzy cluster analysis uses membership degrees to assign data objects to clusters in order to better handle ambiguous data that share properties of different clusters. However, the introduction of membership degrees requires a new parameter called fuzzifier. In this paper the good and bad effects of the fuzzifier on the clustering results are analysed and based on these considerations a more general approach to fuzzy clustering is proposed, providing better control on the membership degrees and their influence in fuzzy cluster analysis.
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Klawonn, F. (2006). Understanding and Controlling the Membership Degrees in Fuzzy Clustering. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_54
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DOI: https://doi.org/10.1007/3-540-31314-1_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31313-7
Online ISBN: 978-3-540-31314-4
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