Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Cluster Analysis by Binary Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering with multilayer perceptrons and self-organized (Hebbian) learning
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - VIII Brazilian Symposium on Neural Networks
Ensemble clustering with voting active clusters
Pattern Recognition Letters
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Resampling-based selective clustering ensembles
Pattern Recognition Letters
Channel estimation by symmetrical clustering
IEEE Transactions on Signal Processing
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
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In recent years, the cluster ensembles have been successfully used to tackle well known drawbacks of individual clustering algorithms. Beyond the expected improvement provided by the averaging effect of many clustering algorithms (clustering committee) aiming at the same goal, some interesting experimental results also show that even committees of completely random partitions may lead to a useful consensus. Another powerful finding in cluster ensemble research is that the blind criterion Averaged Normalized Mutual Information seems to replace actual misclassification ratio, whenever labels are given to actual clusters. In this work, we study what is behind these interesting results and the blind criterion, and we use what we learn from this study to propose a new point of view for analysis and design of clustering committees. The usefulness of this new perspective is illustrated through experimental results.