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Clustering uncertain data has emerged as a challenging task in uncertain datamanagement and mining. Thanks to a computational complexity advantage overother clustering paradigms, partitional clustering has been particularlystudied and a number of algorithms have been developed. While existingproposals differ mainly in the notions of cluster centroid and clusteringobjective function, little attention has been given to an analysis of theircharacteristics and limits. In this work, we theoretically investigate majorexisting methods of partitional clustering, and alternatively propose awell-founded approach to clustering uncertain data based on a novel notion ofcluster centroid. A cluster centroid is seen as an uncertain object defined interms of a random variable whose realizations are derived based on alldeterministic representations of the objects to be clustered. As demonstratedtheoretically and experimentally, this allows for better representing a clusterof uncertain objects, thus supporting a consistently improved clusteringperformance while maintaining comparable efficiency with existing partitionalclustering algorithms.

Uncertain Centroid based Partitional Clustering of Uncertain Data