An Approach for clustering uncertain data objects: A Survey

Recently, uncertain data objects is used in various applications such as VANET environment, sensors applications, image processing based system etc. Clustering of uncertain data is a major concept in data mining since more and more applications, such as sensor database, location database, biometric information systems, and produce vague and imprecise data. Clustering of uncertain data objects is a challenge in spatial data bases. Clustering is a process of organizing objects into groups whose members are similar in some way. There are lots of approaches used to classify the uncertain data by hard classifiers; few of them address the classification of the uncertain data by soft classifier. This paper describes the clustering of uncertain dataset .clustering of object is done by using indexing technique. Probability Density Functions (PDF) is used to represent uncertain data objects. K-Means algorithm is used to generate the clusters. Voronoi diagram is an important technique for answering nearest-neighbor queries for spatial databases. To improve the performance of k-Means, this algorithm is combined with Voronoi diagram.In this paper, we study how the Voronoi diagram can be used on uncertain data, which are inherent in scientific and business applications. Then we conclude our clustering approach.