A review and proposal of (fuzzy) clustering for nonlinearly separable data
In many practical situations data may be characterized by nonlinearly separable clusters. Classical (hard or fuzzy) clustering algorithms produce a partition of objects by computing the Euclidean distance. As such, they are based on the linearity assumption and, therefore, do not identify properly clusters characterized by nonlinear structures. To overcome this limitation, several approaches can be followed: density-, kernel-, graph- or manifold-based clustering.