Similar to (i iii), but applying flat, i.e. non-hieracrhical aggregation instead of hierarchical one, for mean, median and fuzzy AND rules.
For the fuzzy c-means and fuzzy rough c-means algorithms, the fuzziness exponent α was set to 1.2.
Another study proposed in [26] combines the self-clustering algorithm (SCA), fuzzy C-mean and fuzzy rules to process the spatial information and Canny algorithms to get good edge detection.
Outlier detection approaches based on k-means and fuzzy c-means approaches finds variation of data object from the centroid.
This method proved to be superior to the k-means and fuzzy C means clustering methods, and has potential value in real-world applications.
Clustering results are compared with k-means and fuzzy c-means with clustering validation using Davies Bouldin index and Silhouette coefficient.
The problem in implementation of DBSCAN is selection of global parameter while k-means and fuzzy c-means are based on iterative control scheme.
However, classical k-means and fuzzy c-means clustering methods rely on the user to provide the number of clusters and initial centroids.
The second algorithm tests two different classifiers, called K-means and Fuzzy C-means, to automatically extract information about the areas of concrete where biological crusts present.
