Algorithms for clustering data
Algorithms for clustering data
The Strength of Weak Learnability
Machine Learning
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
ACM Computing Surveys (CSUR)
Distributed clustering using collective principal component analysis
Knowledge and Information Systems
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
A Multi-clustering Fusion Algorithm
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Evidence Accumulation Clustering Based on the K-Means Algorithm
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
A clustering method based on boosting
Pattern Recognition Letters
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Collaborative clustering with background knowledge
Data & Knowledge Engineering
A hierarchical clusterer ensemble method based on boosting theory
Knowledge-Based Systems
| Hi-index | 0.10 |
A promising direction for more robust clustering is to derive multiple candidate clusterings over a common set of objects and then combine them into a consolidated one, which is expected to be better than any candidate. Given a candidate clustering set, we show that with a particular pairwise potential used in Markov random fields, the maximum likelihood estimation is the one closest to the set in terms of a metric distance between clusterings. To minimize such a distance, we present two combining methods based on the new similarity determined by the whole candidate set. We evaluate them on both artificial and real datasets, with candidate clusterings either from full space or subspace. Experiments show that they not only lead to a closer distance to the candidate set, but also achieve a smaller or comparable distance to the true clustering.