Technique For Clustering Uncertain Data Based On Probability Distribution Similarity

: Clustering on uncertain data, one of the essential tasks in data mining. The traditional algorithms like K-Means clustering, UK Means clustering, density based clustering etc, to cluster uncertain data are limited to using geometric distance based similarity measures and cannot capture the difference between uncertain data with their distributions. Such methods cannot handle uncertain objects that are geometrically indistinguishable, such as products with the same mean but very different variances in customer ratings. In the case of K medoid clustering of uncertain data on the basis of their KL divergence similarity, they cluster the data based on their probability distribution similarity. Several methods have been proposed for the clustering of uncertain data. Some of these methods are reviewed. Compared to the traditional clustering methods, K-Medoid clustering algorithm based on KL divergence similarity is more efficient. First the probability distribution method for model uncertain data object then after that measure the similarity between data objects using distance metrics, then finally best clustering methods such as partition clustering, density based clustering. This paper proposes a new met for making the algorithm more effective with the consideration of effective with the consideration of initial selection of med