Similarity based data analysis - methods and applications
Frank-Michael Schleif
University of Bielefeld

Abstract:

Prototype based models offer an intuitive interface to given data sets by means of an inspection of the model prototypes. Many of these methods are however limited to the analysis of Euclidean vectors and can not be used if data are characterized by general (dis-) similarities such as problems specific measures. In this talk we provide an overview on relational extensions of modern prototype methods. We review applications for clustering, classification and visualization employing similarity data.

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