A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Optimized ensembles for clustering noisy data
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
| Hi-index | 0.00 |
Consensus clustering refers to combining multiple clusterings over a common dataset into a consolidated better one. This paper compares three graph partitioning based methods. They differ in how to summarize the clustering ensemble in a graph. They are evaluated in a series of experiments, where component clusterings are generated by tuning parameters controlling their quality and resolution. Finally the combination accuracy is analyzed as a function of the learning dynamics vs. the number of clusterings involved