The Strength of Weak Learnability
Machine Learning
Spectral partitioning: the more eigenvectors, the better
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
Machine Learning
ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stable algorithms for link analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Ensembling neural networks: many could be better than all
Artificial Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Bagging for Path-Based Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Ensembles of Partitions via Data Resampling
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moderate diversity for better cluster ensembles
Information Fusion
A tutorial on spectral clustering
Statistics and Computing
Statistical Analysis and Data Mining
Resampling-based selective clustering ensembles
Pattern Recognition Letters
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering aggregation by probability accumulation
Pattern Recognition
Adaptive cluster ensemble selection
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Knowledge-Based Systems
Semi-supervised clustering ensemble based on multi-ant colonies algorithm
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Statistical shape model for manifold regularization: Gleason grading of prostate histology
Computer Vision and Image Understanding
HMM-based hybrid meta-clustering ensemble for temporal data
Knowledge-Based Systems
| Hi-index | 0.10 |
Traditional clustering ensemble methods combine all obtained clustering results at hand. However, we can often achieve a better clustering solution if only parts of the clustering results available are combined. In this paper, we generalize the selective clustering ensemble algorithm proposed by Azimi and Fern and a novel clustering ensemble method, SELective Spectral Clustering Ensemble (SELSCE), is proposed. The component clusterings of the ensemble system are generated by spectral clustering (SC) capable of engendering diverse committees. The random scaling parameter, Nystrom approximation are used to perturb SC for producing the components of the ensemble system. After the generation of component clusterings, the bagging technique, usually applied in supervised learning, is used to assess the component clustering. We randomly pick part of the available clusterings to get a consensus result and then compute normalized mutual information (NMI) or adjusted rand index (ARI) between the consensus result and the component clusterings. Finally, the components are ranked by aggregating multiple NMI or ARI values. The experimental results on UCI dataset and images demonstrate that the proposed algorithm can achieve a better result than the traditional clustering ensemble methods.