PCA-guided fuzzy cluster validation with noise rejection

This paper considers cluster validation for fuzzy clustering with noise rejection. Although noise rejection mechanisms such as noise fuzzy clustering or graded possibilistic noise rejection make it possible to remove the influence of noisy samples, they also create problems in applying conventional validity measures designed for fuzzy clustering with probabilistic constraints. In this paper, a PCA-guided validation approach is developed, in which a rotated optimal cluster indicator is derived in a fuzzy PCA-guided manner, considering responsibility weights for c-means clustering. The deviation between a current solution and the optimal solution is estimated through procrustean transformation. Several experimental results demonstrate that the proposed validation approach works well for selecting both the optimal initialization and the cluster number.