NP-hard problems in hierarchical-tree clustering
Acta Informatica
Journal of Computational Physics
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Cluster analysis and mathematical programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Meta-Heuristics: Theory and Applications
Meta-Heuristics: Theory and Applications
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization
Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization
Essays and Surveys in Metaheuristics
Essays and Surveys in Metaheuristics
A modified noising algorithm for the graph partitioning problem
Integration, the VLSI Journal
The noising method: a new method for combinatorial optimization
Operations Research Letters
Consensus clustering using spectral theory
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Lagrangian relaxation and pegging test for the clique partitioning problem
Advances in Data Analysis and Classification
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This paper deals with the application of noising methods to a clique partitioning problem for a weighted graph. The aim is to study different ways to add noise to the data, and to show that the choice of the noise-adding-scheme may have some impact on the performance of these methods. Among the noise-adding-schemes described here, two of them are totally new, leading to the ''forgotten vertices'' and to the ''forgotten edges'' methods. We also experimentally study a generic noising method that automatically tunes its parameters. For each noise-adding-scheme, we compare a variant which inserts descents and a variant which does not.