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 $e$ and leads to improved approximation guarantees when average element frequency is less than $e$. 
Cosmin Bonchis and Gabriel Istrate
cs.DS cs.CC