NP-hard problems in hierarchical-tree clustering
Acta Informatica
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Mathematical Programming: Series A and B
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Information Processing Letters
Substitution Decomposition on Chordal Graphs and Applications
ISA '91 Proceedings of the 2nd International Symposium on Algorithms
Cluster graph modification problems
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
Graph-Modeled Data Clustering: Exact Algorithms for Clique Generation
Theory of Computing Systems
Deterministic algorithms for rank aggregation and other ranking and clustering problems
WAOA'07 Proceedings of the 5th international conference on Approximation and online algorithms
Exact algorithms for cluster editing: evaluation and experiments
WEA'08 Proceedings of the 7th international conference on Experimental algorithms
The cluster editing problem: implementations and experiments
IWPEC'06 Proceedings of the Second international conference on Parameterized and Exact Computation
A more effective linear kernelization for Cluster Editing
ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
Fixed-Parameter Algorithms for Graph-Modeled Date Clustering
TAMC '09 Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation
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
Graph-Based Data Clustering with Overlaps
COCOON '09 Proceedings of the 15th Annual International Conference on Computing and Combinatorics
Generalized graph clustering: recognizing (p, q)-cluster graphs
WG'10 Proceedings of the 36th international conference on Graph-theoretic concepts in computer science
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The goal of the Cluster Editingproblem is to make the fewest changes to the edge set of an input graph such that the resulting graph is a disjoint union of cliques. This problem is NP-complete but recently, several parameterized algorithms have been proposed. In this paper we present a surprisingly simple branching strategy for Cluster Editing. We generalize the problem assuming that edge insertion and deletion costs are positive integers. We show that the resulting search tree has size O(1.82k) for edit cost k, resulting in the currently fastest parameterized algorithm for this problem. We have implemented and evaluated our approach, and find that it outperforms other parametrized algorithms for the problem.