Moderate diversity for better cluster ensembles
Information Fusion
Fuzzy clustering ensemble based on mutual information
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Improved search strategies and extensions to k-medoids-based clustering algorithms
International Journal of Business Intelligence and Data Mining
Reliability Assessment of Ensemble Classifiers: Application in Mammography
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
Belief Functions and Cluster Ensembles
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A new efficient approach in clustering ensembles
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Consensus clustering using spectral theory
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Ensemble clustering in the belief functions framework
International Journal of Approximate Reasoning
Nonparametric Bayesian clustering ensembles
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Hybrid ensemble approach for classification
Applied Intelligence
Comparing clustering and metaclustering algorithms
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Integration analysis of diverse genomic data using multi-clustering results
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
A novel framework for discovering robust cluster results
DS'06 Proceedings of the 9th international conference on Discovery Science
Heterogeneous clustering ensemble method for combining different cluster results
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Cluster ensemble selection based on relative validity indexes
Data Mining and Knowledge Discovery
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Ensemble techniques have been successfully applied in the context of supervised learning toincrease the accuracy and stability of classification. Recently, analogous techniques for cluster analysis have been suggested. Research has demonstrated that, by combining a collection of dissimilar clusterings, an improved solution can be obtained. In this paper, we examine the potential of applying ensemble clustering techniques with a focus on the area of medical diagnostics. We present several ensemble generation and integration strategies, and evaluate each approach on a number of synthetic and real-world datasets. In addition, we show that diversity among ensemble members is necessary, but not sufficient to yield an improved solution without the selection of an appropriate integration method.