Abstract
FCM-type linear fuzzy clustering is also useful for local PCA because the clustering algorithm partitions data sets by calculating linear prototypes that can be identified with local principal sub-spaces. However, FCM-type algorithms often suffer from the initialization problem where we have multiple results with different initializations, and we must also pre-define the cluster number. In this research, several validation indices are compared in linear fuzzy clustering tasks and then applied to pareto optimality analysis considering both of minimization of objective function and maximization of cluster validity.
