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Clustering uncertain data is an essential task in data mining for theinternet of things. Possible world based algorithms seem promising forclustering uncertain data. However, there are two issues in existing possibleworld based algorithms: (1) They rely on all the possible worlds and treat themequally, but some marginal possible worlds may cause negative effects. (2) Theydo not well utilize the consistency among possible worlds, since they conductclustering or construct the affinity matrix on each possible worldindependently. In this paper, we propose a representative possible world basedconsistent clustering (RPC) algorithm for uncertain data. First, by introducingrepresentative loss and using Jensen-Shannon divergence as the distributionmeasure, we design a heuristic strategy for the selection of representativepossible worlds, thus avoiding the negative effects caused by marginal possibleworlds. Second, we integrate a consistency learning procedure into spectralclustering to deal with the representative possible worlds synergistically,thus utilizing the consistency to achieve better performance. Experimentalresults show that our proposed algorithm performs better than thestate-of-the-art algorithms.

Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning