Titlebook: Similarity-Based Clustering; Recent Developments Michael Biehl,Barbara Hammer,Thomas Villmann Book 2009 Springer-Verlag Berlin Heidelberg

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Advances in Feature Selection with Mutual Informationnal data. Selecting features helps fighting the curse of dimensionality, improving the performances of prediction or classification methods, and interpreting the application. In a nonlinear context, the mutual information is widely used as relevance criterion for features and sets of features. Never

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Median Topographic Maps for Biomedical Data Setssimilarity 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 effic

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Learning Highly Structured Manifolds: Harnessing the Power of SOMsatistics. We call such data manifolds .. We describe approaches to structure identification through self-organized learning, in the context of such data. We present some of our recently developed methods to show that self-organizing neural maps contain a great deal of information that can be unleash

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Estimation of Boar Sperm Status Using Intracellular Density Distribution in Grey Level Imagesd, preprocessed and segmented in order to obtain images of single sperm heads. A model of intracellular density distribution characteristic of alive cells is computed by averaging a set of images of cells assumed to be alive by veterinarian experts. We quantify the deviation of the distribution of a

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