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Improved approximation algorithms for low-density instances of the Minimum Entropy Set Cover Problem
We study the approximability of instances of the minimum entropy set cover
problem, parameterized by the average frequency of a random element in the
covering sets. We analyze an algorithm combining a greedy approach with another
one biased towards large sets. The algorithm is controled by the percentage of
elements to which we apply the biased approach. The optimal parameter choice
has a phase transition around average density and leads to improved
approximation guarantees when average element frequency is less than