Matrix analysis
Algorithms for clustering data
Algorithms for clustering data
Spectral partitioning with multiple eigenvectors
Discrete Applied Mathematics - Special volume on VLSI
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Spectral clustering algorithms for ultrasound image segmentation
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Weighted adaptive neighborhood hypergraph partitioning for image segmentation
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
New spectral methods for ratio cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Data clustering by scaled adjacency matrix
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Clustering and outlier detection using isoperimetric number of trees
Pattern Recognition
| Hi-index | 0.08 |
This paper presents a new method for solving clustering problem. We treat clustering as a graph-partitioning problem and propose a new global criterion, the optimum cut, for segmenting the graph. An important feature is that optimizing the optimum cut criterion can ensure that the intra-cluster similarity is maximized while the inter-cluster similarity is minimized. We show that an efficient computational technique based on an eigenvalue problem can be used to optimize this criterion. The experimental results on a number of hard artificial and real-world data sets show the effectiveness of the approach.