An experimental comparison of model-based clustering methods
Machine Learning
Self-Organizing Maps
On Clustering Validation Techniques
Journal of Intelligent Information Systems
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 multiple partitions
The Journal of Machine Learning Research
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Gravitational Fuzzy Clustering
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Clustering: A neural network approach
Neural Networks
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Weighted partition consensus via kernels
Pattern Recognition
Bioinformatics
Combining multiple clusterings using similarity graph
Pattern Recognition
Gravitational clustering of the self-organizing map
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Automatic Cluster Detection in Kohonen's SOM
IEEE Transactions on Neural Networks
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Clustering-ensemble methods have emerged recently as an effective approach to the problem of clustering, which is one of the fundamental data-analysis tools. Data clustering with an ensemble involves two steps: generation of the ensemble with single-clustering methods and the combination of the obtained solutions to produce a final consensus partition of the data. In this paper we first propose a novel clustering method, based on Kohonen's self-organising map and gravitational algorithm, and, second, investigate its performance in the generation of a clustering ensemble. The proposed method is able to discover clusters of complex shapes and determines the number of clusters automatically. Furthermore, its stochastic nature is beneficial in the construction of a diverse ensemble of partitions. Promising results of the presented method were obtained in comparison with three, relevant, single-clustering algorithms over artificial and real data sets.