  Clustering uncertain data is an essential task in data mining for the
internet of things. Possible world based algorithms seem promising for
clustering uncertain data. However, there are two issues in existing possible
world based algorithms: (1) They rely on all the possible worlds and treat them
equally, but some marginal possible worlds may cause negative effects. (2) They
do not well utilize the consistency among possible worlds, since they conduct
clustering or construct the affinity matrix on each possible world
independently. In this paper, we propose a representative possible world based
consistent clustering (RPC) algorithm for uncertain data. First, by introducing
representative loss and using Jensen-Shannon divergence as the distribution
measure, we design a heuristic strategy for the selection of representative
possible worlds, thus avoiding the negative effects caused by marginal possible
worlds. Second, we integrate a consistency learning procedure into spectral
clustering to deal with the representative possible worlds synergistically,
thus utilizing the consistency to achieve better performance. Experimental
results show that our proposed algorithm performs better than the
state-of-the-art algorithms.
