Online k-Maxoids clustering

We present an online learning algorithm to extract extremal prototypes from a set of data. As an online algorithm, our method can continue to learn during the application phase of a system. However, as a greedy update procedure, it may be sensitive to outliers. We therefore consider the use of extreme value theory for self-assessment and discuss how to incorporate Weibull statistics so as to increase robustness. We evaluate our approaches on synthetic as well as real world datasets to perform profiling. Our empirical results show that incorporating self-assessment not only results in better data representations but also reveals interpretable insights about the analyzed dataset.