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In past years, many methods have been implemented for maintaining and supervising uncertain data that may occur due to collection of data in new ways which results in missing values, erroneous data. The main aim of this work is to help the end user to get correct information about spatial data. The behaviour of data as an outlier is the result of uncertainty. The challenge in spatial data sets is to cluster uncertain objects. Hence, unsupervised clustering can be used to deal with this type of data. In this paper, the difficulty of outlier detection with uncertain data is examined. To improve the performance and quality, Voronoi Diagram is used which partition the objects into each cell and helps to see the exact location of an object. The integral part is the pre-processing step of removing uncertainty to avoid wrong interpretation. Furthermore, CLARA (Clustering LARge Applications) algorithm is …