Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Multiple comparison procedures
Multiple comparison procedures
Algorithms for clustering data
Algorithms for clustering data
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
On Clustering Validation Techniques
Journal of Intelligent Information Systems
A Supra-Classifier Architecture for Scalable Knowledge Reuse
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Clustering of diverse genomic data using information fusion
Proceedings of the 2004 ACM symposium on Applied computing
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Ensemble Clustering in Medical Diagnostics
CBMS '04 Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Evolutionary Algorithms for Clustering Gene-Expression Data
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
Getting the Most Out of Ensemble Selection
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Moderate diversity for better cluster ensembles
Information Fusion
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Visualization and Computer Graphics
Ensemble clustering with voting active clusters
Pattern Recognition Letters
Statistical Analysis and Data Mining
Adaptive cluster ensemble selection
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Selecting diversifying heuristics for cluster ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Relative clustering validity criteria: A comparative overview
Statistical Analysis and Data Mining
Efficiency issues of evolutionary k-means
Applied Soft Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
ACM SIGKDD Explorations Newsletter
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Cluster ensemble aims at producing high quality data partitions by combining a set of different partitions produced from the same data. Diversity and quality are claimed to be critical for the selection of the partitions to be combined. To enhance these characteristics, methods can be applied to evaluate and select a subset of the partitions that provide ensemble results similar or better than those based on the full set of partitions. Previous studies have shown that this selection can significantly improve the quality of the final partitions. For such, an appropriate evaluation of the candidate partitions to be combined must be performed. In this work, several methods to evaluate and select partitions are investigated, most of them based on relative clustering validity indexes. These indexes select the partitions with the highest quality to participate in the ensemble. However, each relative index can be more suitable for particular data conformations. Thus, distinct relative indexes are combined to create a final evaluation that tends to be robust to changes in the application scenario, as the majority of the combined indexes may compensate the poor performance of some individual indexes. We also investigate the impact of the diversity among partitions used for the ensemble. A comparative evaluation of results obtained from an extensive collection of experiments involving state-of-the-art methods and statistical tests is presented. Based on the obtained results, a practical design approach is proposed to support cluster ensemble selection. This approach was successfully applied to real public domain data sets.