Unsupervised clustering algorithm based on normalized Mahalanobis distances
Jeng-Ming Yih, Sue-Fen Huang · WSEAS international conference on Applied Computer and Applied Computational Science · 2010
Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the data with multivariate Gaussian distribution. Three improved Fuzzy C-Means algorithm based on different Mahalanobis distance, called FCM-M, FCM-CM and FCM-SM were proposed by our previous works, 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 show that our proposed new algorithm has the better performance.