An adaptive isodata fuzzy clustering algorithm with partial supervision

Semi-supervised learning uses large amount of unlabeled data, combined with labeled data, to guide the learning process. This paper introduces a new clustering algorithm with partial supervision 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 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.