Fuzzy c-medoids method based on JS-divergence for uncertain data clustering
Uncertain data clustering is one significant research in data mining. Many similarity measurements of uncertain objects are proposed. Traditional clustering methods can be extended with these new similarity measurements. In this paper, we propose a new fuzzy c-medoids method for uncertain data clustering, named UFC-medoids. The JS-divergence is used as the similarity measurement between uncertain objects in this algorithm. In the experiments on synthetic datasets, the presented algorithm has shown a good performance.