Median topographic maps for biomedical data sets
Similarity-Based Clustering: Recent Developments and Biomedical Applications, 2009•Springer
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.
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.
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.
Springer
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