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Entropy-Based Framework for Dynamic Coverage and Clustering Problems
2012
IEEE Transactions on Automatic Control
We propose a computationally efficient framework to solve a large class of dynamic coverage and clustering problems, ranging from those that arise from deployment of mobile sensor networks to classification of cellular data for diagnosing cancer stages. This framework provides the ability to identify natural clusters in the underlying data set. In particular, we define the problem of minimizing instantaneous coverage as a combinatorial optimization problem in a Maximum Entropy Principle (MEP)
doi:10.1109/tac.2011.2166713
fatcat:465q3hzkczb3bpl6qouzaptkxa