Median topographic maps for biomedical data sets

Barbara Hammer (1) , Alexander Hasenfuss (1) , Fabrice Rossi (2)

1 Institute of Computer Science
2 LTCI - Laboratoire Traitement et Communication de l'Information

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.

Mots clés: Neural Gas; Pairwise data; Dissimilarity data; Medoid; Clustering

Domaines: Apprentissage [cs.LG]; Bio-informatique [q-bio.QM]; Bio-Informatique, Biologie Systémique [q-bio.QM]

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⟩