Popular incremental fuzzy clustering algorithms for data streams include single-pass FCM [27] and online FCM (OFCM) [28].
This in turn leads to faster convergence when compared to FCM [25].
So in proof questions, we can generate weight matrix as in FCM [8].
The SA treatment increased 3.5% fat-corrected milk (3.5% FCM; 40.5 vs. 38.6 kg/d) but did not affect feed efficiency (3.5% FCM/DMI, 1.55 vs. 1.53), body weight, or body condition score compared with the control.
In particular, the fuzzy approach that we propose is based on the fuzzy c-means (FCM) algorithm[10, 11].
Based on these two issues, we present a new cluster validity index capable of being used for type-1 Fuzzy C-Means (FCMs), Interval Type-2 FCM (IT2 FCM), and Possibilistic C-Means (PCMs) clustering algorithms.
And the Fringe Center Method (FCM) [6, 7] or the Fourier transform [8, 9] are still an important inspection method to extract the character information of the fringes pattern.
Preliminary studies using fuzzy techniques such as Fuzzy C-Means (FCM) [14] and Hard C-Means (HCM) algorithms [15] have also been reported in literature.
The MNG only diet supported lower yields of FCM (7.7 vs. 10.7 kg cow−1 day−1) than the mean of the supplemented MNG (P<0.001).
Several incremental fuzzy clustering algorithms based on fuzzy c-means (FCM) [8] to track non-stationarity in data streams have been developed.
The most frequently used examples for these c-means clustering categories the k-means or hard c-means (HCM) [28], fuzzy c-means (FCM) [29] and possibilistic c-means (PCM) [30] algorithms.
