An adaptive semi-supervised fuzzy clustering algorithm based on objective function optimization

Semi-supervised learning uses large amount of unlabeled data, combined with the labeled data, to guide the learning process. This paper introduces a new semi-supervised clustering algorithm based on an adaptive distance. The proposed method furnishes a fuzzy partition and a prototype for each cluster by optimizing a criterion based on an adaptive distance allowing the construction of partitions in ellipsoids format, in addition to spherical shape generated by the Euclidean distance. Experiments with real and synthetic data sets show the usefulness of the proposed method by comparing with others adaptive and non-adaptive semi-supervised clustering algorithms in a clustering task.