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DAC '97 Proceedings of the 34th annual Design Automation Conference
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
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FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
A Scalable Approach to Balanced, High-Dimensional Clustering of Market-Baskets
HiPC '00 Proceedings of the 7th International Conference on High Performance 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
A unified framework for model-based clustering
The Journal of Machine Learning Research
Combining Pattern Classifiers: Methods and Algorithms
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Cluster ensemble and its applications in gene expression analysis
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Analysis of Consensus Partition in Cluster Ensemble
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A distributed learning framework for heterogeneous data sources
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Feature diversity in cluster ensembles for robust document clustering
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Clustering with Bregman Divergences
The Journal of Machine Learning Research
ACM Transactions on Knowledge Discovery from Data (TKDD)
An aggregated clustering approach using multi-ant colonies algorithms
Pattern Recognition
Consensus Clustering for Detection of Overlapping Clusters in Microarray Data
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A scalable framework for cluster ensembles
Pattern Recognition
External validation measures for K-means clustering: A data distribution perspective
Expert Systems with Applications: An International Journal
Generalized cluster aggregation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Automatic malware categorization using cluster ensemble
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Nonparametric Bayesian clustering ensembles
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
AP-Based Consensus Clustering for Gene Expression Time Series
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Heterogeneous clustering ensemble method for combining different cluster results
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
C3E: a framework for combining ensembles of classifiers and clusterers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Objective function-based clustering
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Projective clustering ensembles
Data Mining and Knowledge Discovery
Least square consensus clustering: criteria, methods, experiments
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
On the combination of relative clustering validity criteria
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Subsampling for efficient and effective unsupervised outlier detection ensembles
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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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Cluster ensembles combine multiple clusterings of a set of objects into a single consolidated clustering, often referred to as the consensus solution. Consensus clustering can be used to generate more robust and stable clustering results compared to a single clustering approach, perform distributed computing under privacy or sharing constraints, or reuse existing knowledge. This paper describes a variety of algorithms that have been proposed to address the cluster ensemble problem, organizing them in conceptual categories that bring out the common threads and lessons learnt while simultaneously highlighting unique features of individual approaches. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 305–315 DOI: 10.1002/widm.32