NP-hard problems in hierarchical-tree clustering
Acta Informatica
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Algorithm Design
Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
The complexity of Kemeny elections
Theoretical Computer Science
Invitation to data reduction and problem kernelization
ACM SIGACT News
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Multiple genome rearrangement by swaps and by element duplications
Theoretical Computer Science
On the Approximation of Correlation Clustering and Consensus Clustering
Journal of Computer and System Sciences
Aggregating inconsistent information: Ranking and clustering
Journal of the ACM (JACM)
Pattern matching with address errors: Rearrangement distances
Journal of Computer and System Sciences
Computing slater rankings using similarities among candidates
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Improved bounds for computing Kemeny rankings
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Deterministic Pivoting Algorithms for Constrained Ranking and Clustering Problems
Mathematics of Operations Research
Fixed-parameter algorithms for Kemeny rankings
Theoretical Computer Science
Closest Substring Problems with Small Distances
SIAM Journal on Computing
Towards Fully Multivariate Algorithmics: Some New Results and Directions in Parameter Ecology
Combinatorial Algorithms
Kernelization: New Upper and Lower Bound Techniques
Parameterized and Exact Computation
Improved Parameterized Algorithms for the Kemeny Aggregation Problem
Parameterized and Exact Computation
On the parameterized complexity of consensus clustering
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
Studies in computational aspects of voting: open problems of downey and fellows
The Multivariate Algorithmic Revolution and Beyond
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We propose an effective polynomial-time preprocessing strategy for intractable median problems. Developing a new methodological framework, we show that if the input objects of generally intractable problems exhibit a sufficiently high degree of similarity between each other on average, then there are efficient exact solving algorithms. In other words, we show that the median problems Swap Median Permutation, Consensus Clustering, Kemeny Score, and Kemeny Tie Score all are fixed-parameter tractable with respect to the parameter ''average distance between input objects''. To this end, we develop the novel concept of ''partial kernelization'' and, furthermore, identify polynomial-time solvable special cases for the considered problems.