Machine Learning
Why so many clustering algorithms: a position paper
ACM SIGKDD Explorations Newsletter
Ensembling neural networks: many could be better than all
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving Performance in Neural Networks Using a Boosting Algorithm
Advances in Neural Information Processing Systems 5, [NIPS Conference]
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Feature Weighting in k-Means Clustering
Machine Learning
Pose Invariant Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
Multisource images analysis using collaborative clustering
EURASIP Journal on Advances in Signal Processing
A new method for hierarchical clustering combination
Intelligent Data Analysis
Fragment-based clustering ensembles
Proceedings of the 18th ACM conference on Information and knowledge management
A Combine-Correct-Combine Scheme for Optimizing Dissimilarity-Based Classifiers
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Semi-supervised Classification Based on Clustering Ensembles
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Clustering ensembles based on normalized edges
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations
IEEE Transactions on Fuzzy Systems
Voting-averaged combination method for regressor ensemble
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Optimized ensembles for clustering noisy data
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Bagging-based spectral clustering ensemble selection
Pattern Recognition Letters
Bucket Learning: Improving model quality through enhancing local patterns
Knowledge-Based Systems
Joint cluster based co-clustering for clustering ensembles
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Spectral clustering with discriminant cuts
Knowledge-Based Systems
Cluster ensembles in collaborative filtering recommendation
Applied Soft Computing
Constraint projections for semi-supervised affinity propagation
Knowledge-Based Systems
A hierarchical clusterer ensemble method based on boosting theory
Knowledge-Based Systems
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Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods for unsupervised learning. Here, an ensemble comprises multiple clusterers, each of which is trained by k-means algorithm with different initial points. The clusters discovered by different clusterers are aligned, i.e. similar clusters are assigned with the same label, by counting their overlapped data items. Then, four methods are developed to combine the aligned clusterers. Experiments show that clustering performance could be significantly improved by ensemble methods, where utilizing mutual information to select a subset of clusterers for weighted voting is a nice choice. Since the proposed methods work by analyzing the clustering results instead of the internal mechanisms of the component clusterers, they are applicable to diverse kinds of clustering algorithms.