Obermeier, SandraBeer, AnnaWahl, FlorianSeidl, ThomasKai-Uwe SattlerMelanie HerschelWolfgang Lehner2021-03-162021-03-162021978-3-88579-705-0https://dl.gi.de/handle/20.500.12116/35814Even though most clustering algorithms serve knowledge discovery in fields other than computer science, most of them still require users to be familiar with programming or data mining to some extent. As that often prevents efficient research, we developed an easy to use, highly explainable clustering method accompanied by an interactive tool for clustering. It is based on intuitively understandable kNN graphs and the subsequent application of adaptable filters, which can be combined ensemble-like and iteratively and prune unnecessary or misleading edges. For a first overview of the data, fully automatic predefined filter cascades deliver robust results. A selection of simple filters and combination methods that can be chosen interactively yield very good results on benchmark datasets compared to various algorithms.enClusteringInteractivekNNEnsembleExplainability10.18420/btw2021-091617-5468