A novel distributed semi-supervised fuzzy clustering method applied on dental X-ray images

Semi-supervised clustering methods are applied in various fields. However, these methods have the limitations when the size of data is large. Thus, distributed clustering models are proposed. By separating data set into some parts, distributed models overcome the challenges of big size dataset. In this paper, a novel model, based on semi-supervised fuzzy clustering and distributed mechanism, is proposed. In our research, the method to define distributed additional information is introduced. This additional information is used to implement distributed semi-supervised fuzzy clustering model on three datasets. The main contribution in this paper is the proposal of distributed semi-supervised fuzzy clustering algorithm (denoted as DSSFCM) that performs on Master – Slave model on homogeneous datasets.