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Median topographic maps for biomedical data sets


Median clustering extends popular neural data analysis methods such as the self-organizing map or neural gas to general data structures given by a dissimilarity matrix only. This offers flexible and robust global data inspection methods which are particularly suited for a variety of data as occurs in biomedical domains. In this chapter, we give an overview about median clustering and its properties and extensions, with a particular focus on efficient implementations adapted to large scale data analysis.
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hal-00413148 , version 1 (03-09-2009)



Barbara Hammer, Alexander Hasenfuss, Fabrice Rossi. Median topographic maps for biomedical data sets. Villmann, Th.; Biehl, M.; Hammer, B.; Verleysen, M. Similarity-Based Clustering, Springer Berlin / Heidelberg, pp.92-117, 2009, Lecture Notes in Computer Science, ⟨10.1007/978-3-642-01805-3_6⟩. ⟨hal-00413148⟩
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