Data mining problems are significantly influenced by the uncertainty in data. Clustering certain data has been well studied in many field of data mining, but there is an only preliminary study in clustering uncertain data. Traditional clustering algorithms are mainly on geometric locations. So such methods will not able to find the similarity of uncertain objects that have different distribution and geometrically indistinguishable. In this paper we introduce a divergence method called KL-divergence for finding the similarity of uncertain objects. And this similarity is integrated into both density based and partition based clustering. And also we are comparing the accuracy level of both clustering methods using KL-divergence and using geometric distances as similarity measure and will find better and efficient method for clustering the uncertain objects.