Clustering is widely used in text analysis, natural language processing, image segmentation, and other data mining fields.
The Gaussian mixture model (GMM) provides a convenient yet principled framework for clustering, with properties suitable for statistical inference.
In the application of FCMdd and original ECMdd, a single medoid (prototype), which is supposed to belong to the object set, is utilized to represent one class.
We propose a credal classification method for incomplete pattern with adaptive imputation of missing values based on belief function theory.
In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical framework of belief functions is proposed.