An Analytical Review on Rough Set Based Image Clustering

Clustering is one of the most vital image segmentation techniques. However, proper image clustering has always been a challenging task due to blurred and vague areas near to concerned object boundaries. Therefore, rough set based clustering techniques like Rough k-means (RKM) has been employed in image clustering domain because rough set concept can handle the overlapping clusters to a great extent. RKM shows the performance in image clustering domain as a similarity based clustering model like K-Means and Fuzzy C-Means. Therefore, this paper presents an up-to-date review on rough set based image clustering approaches with their merits and demerits. The measures of similarity as well as the evaluation criteria for rough clustering are also discussed in this study. Beside that the key issues which are involved during the development of rough set based clustering models are investigated in this paper.