We consider the clustering aggregation problem in which we are given a set of
clusterings and want to find an aggregated clustering which minimizes the sum
of mismatches to the input clusterings. In the binary case (each clustering is
a bipartition) this problem was known to be NP-hard under Turing reduction. We
strengthen this result by providing a polynomial-time many-one reduction. Our
result also implies that no 2o(n)β β£Iβ£O(1)-time algorithm exists
for any clustering instance I with n elements, unless the Exponential Time
Hypothesis fails. On the positive side, we show that the problem is
fixed-parameter tractable with respect to the number of input clusterings