Fuzzy Sets and Systems - Special issue: fuzzy sets and management science
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Cluster ensembles: a knowledge reuse framework for combining partitionings
Eighteenth national conference on Artificial intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Bagging for Path-Based Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Cluster ensemble and its applications in gene expression analysis
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Ensembles of Partitions via Data Resampling
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Ensemble Clustering in Medical Diagnostics
CBMS '04 Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems
Landscape of Clustering Algorithms
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Editorial: Identity fusion in unsupervised environments
Information Fusion
Definition of MV load diagrams via weighted evidence accumulation clustering using subsampling
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
Definition of MV load diagrams via weighted evidence accumulation clustering using subsampling
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
CONSENSUS-BASED ENSEMBLES OF SOFT CLUSTERINGS
Applied Artificial Intelligence
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Resampling-based selective clustering ensembles
Pattern Recognition Letters
A new method for hierarchical clustering combination
Intelligent Data Analysis
Fuzzy ensemble clustering based on random projections for DNA microarray data analysis
Artificial Intelligence in Medicine
Belief Functions and Cluster Ensembles
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses
Artificial Intelligence in Medicine
Adaptive cluster ensemble selection
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations
IEEE Transactions on Fuzzy Systems
Confusion matrix disagreement for multiple classifiers
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Consensus clustering using spectral theory
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Information theoretic combination of pattern classifiers
Pattern Recognition
Learn++.MF: A random subspace approach for the missing feature problem
Pattern Recognition
On combining multiple clusterings: an overview and a new perspective
Applied Intelligence
Ensemble clustering in the belief functions framework
International Journal of Approximate Reasoning
Optimized ensembles for clustering noisy data
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Cluster ensemble in adaptive tree structured clustering
International Journal of Knowledge Engineering and Soft Data Paradigms
Bagging-based spectral clustering ensemble selection
Pattern Recognition Letters
A latent variable pairwise classification model of a clustering ensemble
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Ensembles based on random projections to improve the accuracy of clustering algorithms
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Cluster ensembles via weighted graph regularized nonnegative matrix factorization
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
From cluster ensemble to structure ensemble
Information Sciences: an International Journal
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
Subsampling for efficient and effective unsupervised outlier detection ensembles
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster ensemble selection based on relative validity indexes
Data Mining and Knowledge Discovery
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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Adjusted Rand index is used to measure diversity in cluster ensembles and a diversity measure is subsequently proposed. Although the measure was found to be related to the quality of the ensemble, this relationship appeared to be non-monotonic. In some cases, ensembles which exhibited a moderate level of diversity gave a more accurate clustering. Based on this, a procedure for building a cluster ensemble of a chosen type is proposed (assuming that an ensemble relies on one or more random parameters): generate a small random population of cluster ensembles, calculate the diversity of each ensemble and select the ensemble corresponding to the median diversity. We demonstrate the advantages of both our measure and procedure on 5 data sets and carry out statistical comparisons involving two diversity measures for cluster ensembles from the recent literature. An experiment with 9 data sets was also carried out to examine how the diversity-based selection procedure fares on ensembles of various sizes. For these experiments the classification accuracy was used as the performance criterion. The results suggest that selection by median diversity is no worse and in some cases is better than building and holding on to one ensemble.