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Secondly, the FCM algorithm is introduced.
The FCM data corroborated with the NMR data.
Fig. 5 The clustering results from the FCM method.
Outlier sensitivity is one shortcoming of the FCM clustering.
Hence, a straightforward extension of the FCM is needed.
However, the FCM was the most suitable method.
However, the standard deviation is more than twice that of the FCM clustering, and this suggests that the FCM delivers more consistent and reliable separation of the sources.
The FCM clustering suggested the existence of five optically different water classes in the region.
In general, the FCM for mask estimation proved the most robust.
The output of the FCM clustering is a fuzzy membership partition matrix [21, 33].
This paper is focused on the second shortcoming of the FCM algorithm.
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