Competitive learning algorithms for vector quantization
Neural Networks
Self-Organizing Maps
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Partial Clustering for Tissue Segmentation in MRI
Advances in Neuro-Information Processing
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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Clustering is a classical tool in image analysis, with wide applications. Yet, most of its algorithmic solutions include a considerable amount of stochasticity, e.g. due to different initialisations. Here, we introduce a clustering method rooted on self organizing maps, that exploits the maps' intrinsic variability, to produce reliable clustering. Although only a subset of the data is consistently clustered, we show that this set is trustworthy, and can be used for posterior classification.