Non-redundant clustering with conditional ensembles
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moderate diversity for better cluster ensembles
Information Fusion
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A statistical model of cluster stability
Pattern Recognition
Ensemble clustering with voting active clusters
Pattern Recognition Letters
Boosting for Model-Based Data Clustering
Proceedings of the 30th DAGM symposium on Pattern Recognition
Resampling-based selective clustering ensembles
Pattern Recognition Letters
A new method for hierarchical clustering combination
Intelligent Data Analysis
A scalable framework for cluster ensembles
Pattern Recognition
Comparing hard and fuzzy c-means for evidence-accumulation clustering
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Selecting diversifying heuristics for cluster ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations
IEEE Transactions on Fuzzy Systems
A new efficient approach in clustering ensembles
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Ensemble learning based distributed clustering
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Expert Systems with Applications: An International Journal
Soft spectral clustering ensemble applied to image segmentation
Frontiers of Computer Science in China
Journal of Intelligent Manufacturing
Bagging-based spectral clustering ensemble selection
Pattern Recognition Letters
Classifier selection by clustering
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
A new classifier ensembles framework
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Combining multiple clusterings via k-modes algorithm
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Boosting GMM and its two applications
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Cluster-Based cumulative ensembles
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Cluster ensembles in collaborative filtering recommendation
Applied Soft Computing
From cluster ensemble to structure ensemble
Information Sciences: an International Journal
Computer Methods and Programs in Biomedicine
Adaptive evidence accumulation clustering using the confidence of the objects' assignments
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
A hierarchical clusterer ensemble method based on boosting theory
Knowledge-Based Systems
Data weighing mechanisms for clustering ensembles
Computers and Electrical Engineering
A probabilistic approach to latent cluster analysis
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
An ensemble-clustering-based distance metric and its applications
International Journal of Business Intelligence and Data Mining
Effects of resampling method and adaptation on clustering ensemble efficacy
Artificial Intelligence Review
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The combination of multiple clusterings is a difficultproblem in the practice of distributed data mining. Boththe cluster generation mechanism and the partitionintegration process influence the quality of thecombinations. In this paper we propose a dataresampling approach for building cluster ensembles thatare both robust and stable. In particular, we investigatethe effectiveness of a bootstrapping technique inconjunction with several combination algorithms. Theempirical study shows that a meaningful consensuspartition for an entire set of objects emerges frommultiple clusterings of bootstrap samples, given optimalcombination algorithm parameters. Experimental resultsfor ensembles with varying numbers of partitions andclusters are reported for simulated and real data sets.Experimental results show improved stability andaccuracy for consensus partitions obtained via abootstrapping technique.