Rough Entropy Hierarchical Agglomerative Clustering in Image Segmentation

In data clustering there is a constant demand on development of new algorithmic schemes capable of robust and correct data handling. This demand has been additionally highly fueled and increased by emerging new technologies in data imagery area,. Hierarchical clustering represents established data grouping technique with a wide spectrum of application, especially in image analysis branch. In the paper, a new algorithmic rough entropy framework has been applied in the hierarchical clustering setting. During cluster merges the quality of the resultant merges has been assessed on the base of the rough entropy. Incorporating rough entropy measure as the evaluation of cluster quality takes into account inherent uncertainty, vagueness and impreciseness. The experimental results suggest that hierarchies created during rough entropy based merging process are robust and of high quality, giving possible area for future research applications in real implementations.