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Motivated by the design of low-complexity distributed quantizers and iterative decoding algorithms that leverage the correlation in the data picked up by a large-scale sensor network, we address the problem of finding correlation preserving clusters. To construct a factor graph describing the statistical dependencies between sensor measurements, we develop a hierarchical clustering algorithm that minimizes the Kullback Leibler Distance between known and approximated source statistics. Finally,doi:10.1109/glocom.2006.54 dblp:conf/globecom/MaierbacherB06 fatcat:xgiftmduz5bivlu5ksqq65gjay