Accelerating kernel clustering for biomedical data analysis

The increasing size and complexity of modern data sets turns modern data mining techniques to indispensable tools when inspecting biomedical data sets. Thereby, dedicated data formats and detailed information often cause the need for problem specific similarities or dissimilarities instead of the standard Euclidean norm. Therefore, a number of clustering techniques which rely on similarities or dissimilarities only have recently been proposed. In this contribution, we review some of the most popular dissimilarity based clustering techniques and we discuss possibilities how to get around the usually squared complexity of the models due to their dependency on the full dissimilarity matrix. We evaluate the techniques on two benchmarks from the biomedical domain.