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Research Article Open Access
Clustering on uncertain data, one of the essential tasks in mining uncertain data, posts significant challenges on both modelling similarity between uncertain objects and developing efficient computational methods. The existing methods extend traditional partitioning clustering methods like k-means and density-based clustering methods like DBSCAN and Kullback-Leibler to uncertain data, thus rely on numerical distances between objects. We study the Problem of clustering data objects whose locations are uncertain. A data object is represented by an uncertainty region over which a probability density function (pdf) is defined. The proposed method is based on the maximization of a generalized probability criterion, which can be interpreted as a degree of agreement between the numerical model and the uncertain clarification. We propose a variant of the PM algorithm that iteratively maximizes this measure. As an illustration, the method is applied to uncertain data clustering using finite mixture models, in the cases of categorical and continuous attributes. Our extensive experiment results verify the effectiveness, efficiency, and scalability of our approaches.