Normalized Clustering Algorithm Based on Mahalanobis Distance
نویسندگان
چکیده
FCM (fuzzy c-means algorithm) based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. The added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. In this paper, an improved Normalized Clustering Algorithm Based on Mahalanobis distance by taking a new threshold value and a new convergent process is proposed.
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