Towards practical `neural' computation for combinatorial optimization problems
AIP Conference Proceedings 151 on Neural Networks for Computing
A cutting plane algorithm for a clustering problem
Mathematical Programming: Series A and B
Machine Learning
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
Computers and Operations Research
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
IEEE Transactions on Pattern Analysis and Machine Intelligence
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
Data Clustering Using Evidence Accumulation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Mining hidden community in heterogeneous social networks
Proceedings of the 3rd international workshop on Link discovery
A survey of kernel and spectral methods for clustering
Pattern Recognition
A tutorial on spectral clustering
Statistics and Computing
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new graph-theoretic approach to clustering and segmentation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
The university of Florida sparse matrix collection
ACM Transactions on Mathematical Software (TOMS)
Normalized Cuts for Predominant Melodic Source Separation
IEEE Transactions on Audio, Speech, and Language Processing
| Hi-index | 0.01 |
Normalized cut is one of the most popular graph clustering criteria. The main approaches proposed for its resolution are spectral clustering methods and a multilevel approach of Dhillon et al. (TPAMI 29:1944-1957, 2007), called graclus. Their aim is to obtain good solutions in a small amount of time for large instances. Metaheuristics are general frameworks for stochastic searches often employed in global optimization to improve the solutions obtained by other heuristics. Variable neighborhood search (VNS) is a metaheuristic which exploits systematically the idea of neighborhood change during the search. In this paper, we propose a VNS heuristic for normalized cut segmentation. Computational experiments show that in most cases this VNS heuristic improves significantly, and in moderate time, the solutions obtained by the current state-of-the-art algorithms, i.e., graclus and a spectral method proposed by Yu and Shi (ICCV, 2003).