Generalized fuzzy \(c\)-means clustering and its theoretical properties. (English) Zbl 1517.62069
Torra, Vicenç (ed.) et al., Modeling decisions for artificial intelligence. 15th international conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 11144, 243-254 (2018).
Summary: This study shows that a generalized fuzzy \(c\)-means (gFCM) clustering algorithm, which covers standard fuzzy \(c\)-means clustering, can be constructed if a given fuzzified function, its derivative, and its inverse derivative can be calculated. Furthermore, our results show that the fuzzy classification function for gFCM exhibits similar behavior to that of standard fuzzy \(c\)-means clustering.
For the entire collection see [Zbl 1398.68040].
For the entire collection see [Zbl 1398.68040].
MSC:
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
| 62H86 | Multivariate analysis and fuzziness |
References:
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