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The authors investigate algorithms for clustering of epileptic electroencephalogram (EEG) spikes. Such a method is useful prior to averaging and inverse computations since the spikes of a patient often belong to a few distinct classes. Data sets often contain outliers, which makes algorithms with robust performance desirable. The authors compare the fuzzy C-means (FCM) algorithm and a graph-theoretic algorithm. They give criteria for determination of the correct level of outlier contamination. The performance is then studied by aid of simulations, which show good results for a range of circumstances, for both algorithms. The graph-theoretic method gave better results than FCM for simulated signals. Also, when evaluating the methods on seven real-life data sets, the graph-theoretic method was the better method, in terms of closeness to the manual assessment by a neurophysiologist. However, there was some discrepancy between manual and automatic clustering and the authors suggest as an alternative method a human choice among a limited set of automatically obtained clusterings. Furthermore, the authors evaluate geometrically weighted feature extraction and conclude that it is useful as a supplementary dimension for clustering.