GTM: the generative topographic mapping
Neural Computation
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
On the improvement of brain tumour data clustering using class information
Proceedings of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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This paper analyzes, through clustering and visualization, Magnetic Resonance Spectra corresponding to a complex human brain tumour dataset. Clustering is performed as a two-stage process, in which the first stage model is Generative Topographic Mapping (GTM). In semi-supervised settings, class information can be added to refine the clustering process. A class information-enriched variant of GTM, class-GTM, is used here for a first cluster description of the data. The number of clusters used by GTM is usually large for visualization purposes and does not necessarily correspond to the overall class structure. Consequently, in a second stage, clusters are agglomerated using the K-means algorithm with different initialization strategies, some of them defined ad hoc for the GTM models. We aim to evaluate how and under what circumstances the use of class information influences tumour cluster-wise class separability in the final result of the two-stage clustering process.