Non-redundant clustering with conditional ensembles
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Combining partitions by probabilistic label aggregation
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Combining Multiple Clusterings by Soft Correspondence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Comparing Subspace Clusterings
IEEE Transactions on Knowledge and Data Engineering
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting for Model-Based Data Clustering
Proceedings of the 30th DAGM symposium on Pattern Recognition
Weighted Cluster Ensemble Using a Kernel Consensus Function
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Resampling-based selective clustering ensembles
Pattern Recognition Letters
A scalable framework for cluster ensembles
Pattern Recognition
On voting-based consensus of cluster ensembles
Pattern Recognition
Clustering ensembles based on normalized edges
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Weighted partition consensus via kernels
Pattern Recognition
Construction of the ensemble of logical models in cluster analysis
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Hybrid microdata using microaggregation
Information Sciences: an International Journal
Consensus clustering using spectral theory
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Data Mining and Knowledge Discovery
Soft spectral clustering ensemble applied to image segmentation
Frontiers of Computer Science in China
A latent variable pairwise classification model of a clustering ensemble
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Data clustering: a user’s dilemma
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Joint cluster based co-clustering for clustering ensembles
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Positional and confidence voting-based consensus functions for fuzzy cluster ensembles
Fuzzy Sets and Systems
Cluster ensembles in collaborative filtering recommendation
Applied Soft Computing
From cluster ensemble to structure ensemble
Information Sciences: an International Journal
Ensemble methods for biclustering tasks
Pattern Recognition
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Ensemble clustering by means of clustering embedding in vector spaces
Pattern Recognition
Weighted ensemble of algorithms for complex data clustering
Pattern Recognition Letters
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
ACM SIGKDD Explorations Newsletter
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In combination of multiple partitions, one is usually interested in deriving a consensus solution with a quality better than that of given partitions. Several recent studies have empirically demonstrated improved accuracy of clustering ensembles on a number of artificial and real-world data sets. Unlike certain multiple supervised classifier systems, convergence properties of unsupervised clustering ensembles remain unknown for conventional combination schemes. In this paper we present formal arguments on the effectiveness of cluster ensemble from two perspectives. The first is based on a stochastic partition generation model related to re-labeling and consensus function with plurality voting. The second is to study the property of the "mean" partition of an ensemble with respect to a metric on the space of all possible partitions. In both the cases, the consensus solution can be shown to converge to a true underlying clustering solution as the number of partitions in the ensemble increases. This paper provides a rigorous justification for the use of cluster ensemble.