Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
SIAM Journal on Discrete Mathematics
Aggregating inconsistent information: ranking and clustering
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Fitting tree metrics: Hierarchical clustering and Phylogeny
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Ordering by weighted number of wins gives a good ranking for weighted tournaments
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
SIAM Journal on Discrete Mathematics
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Deterministic pivoting algorithms for constrained ranking and clustering problems
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Aggregation of partial rankings, p-ratings and top-m lists
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
ACM SIGACT News
Aggregating inconsistent information: Ranking and clustering
Journal of the ACM (JACM)
Fixed-Parameter Algorithms for Kemeny Scores
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
Going Weighted: Parameterized Algorithms for Cluster Editing
COCOA 2008 Proceedings of the 2nd international conference on Combinatorial Optimization and Applications
How similarity helps to efficiently compute Kemeny rankings
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
A More Relaxed Model for Graph-Based Data Clustering: s-Plex Editing
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
Deterministic Pivoting Algorithms for Constrained Ranking and Clustering Problems
Mathematics of Operations Research
Fixed-parameter algorithms for Kemeny rankings
Theoretical Computer Science
Going weighted: Parameterized algorithms for cluster editing
Theoretical Computer Science
Editing Graphs into Disjoint Unions of Dense Clusters
ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
Exact algorithms for cluster editing: evaluation and experiments
WEA'08 Proceedings of the 7th international conference on Experimental algorithms
Improved algorithms for bicluster editing
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Fixed-parameter algorithms for cluster vertex deletion
LATIN'08 Proceedings of the 8th Latin American conference on Theoretical informatics
Cluster editing problem for points on the real line: A polynomial time algorithm
Information Processing Letters
A 2k Kernel for the cluster editing problem
COCOON'10 Proceedings of the 16th annual international conference on Computing and combinatorics
Using medians to generate consensus rankings for biological data
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Using Kendall-τ meta-bagging to improve protein-protein docking predictions
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
A 2k kernel for the cluster editing problem
Journal of Computer and System Sciences
Kemeny elections with bounded single-peaked or single-crossing width
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We consider ranking and clustering problems related to the aggregation of inconsistent information. Ailon, Charikar, and Newman [1] proposed randomized constant factor approximation algorithms for these problems. Together with Hegde and Jain, we recently proposed deterministic versions of some of these randomized algorithms [2]. With one exception, these algorithms required the solution of a linear programming relaxation. In this paper, we introduce a purely combinatorial deterministic pivoting algorithm for weighted ranking problems with weights that satisfy the triangle inequality; our analysis is quite simple. We then shown how to use this algorithm to get the first deterministic combinatorial approximation algorithm for the partial rank aggregation problem with performance guarantee better than 2. In addition, we extend our approach to the linear programming based algorithms in Ailon et al. [1] and Ailon [3]. Finally, we show that constrained rank aggregation is not harder than unconstrained rank aggregation.