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Median topographic maps for biomedical data sets

Abstract : 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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https://hal.archives-ouvertes.fr/hal-00413148
Contributor : Fabrice Rossi <>
Submitted on : Thursday, September 3, 2009 - 12:06:58 PM
Last modification on : Friday, July 31, 2020 - 10:44:05 AM
Long-term archiving on: : Tuesday, June 15, 2010 - 8:27:23 PM

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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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