| Summary: | Fuzzy C-Means (FCM) is a data clustering technique where the existence of
each data point in a cluster is determined by the degree of membership that is on the
interval [0,1]. One of the deficiencies that exist in the classical FCM method is that
the membership of a data value to a particular cluster depends directly to the
membership value of the data on another cluster, this is caused by the constraint
functions it has.
that
Several new algorithms are developed to improve the performance of the
FCM, including the Adaptive Fuzzy Clustering (FAC) and the Modified Fuzzy C-
Means (MFCM). Meanwhile, a measuring tool used to evaluate the performance of
clustering methods is to use the ratio of standard deviation in the group and the
standard deviation between groups. Based on the results of grouping by using the
data quality of madrasa education, it turns out MFCM method has better
performance when compared with the other two methods
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