FCM algorithm besed on Normalized Mahalanobis distances in image clustering
Jeng-Ming Yih · 2010
The popular fuzzy c-means algorithm (FCM) based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. Gustafson-Kessel(GK) clustering algorithm was developed to detect non-spherical structural clusters. However, GK clustering algorithm needs added constraint of fuzzy covariance matrix, In this paper, an improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance (FCM-NM) by taking a new threshold value and a new convergent process is proposed The experimental results of two real data sets in image classification show that our proposed new algorithm has the better performance.