CLUE is a fast and fully parallelizable density-based clustering algorithm, optimized for high-occupancy scenarios, where the number of clusters is much larger than the average number of hits in a cluster (Rovere et al. 2020). The algorithm uses a grid spatial index for fast querying of neighbors and its timing scales linearly with the number of hits within the range considered. It is currently used in the CMS and CLIC event reconstruction software for clustering calorimetric hits in two dimensions (x,y) based on their energy. CLUE is implemented in C++ and can execute on CPU and GPUs thanks to the Alpaka performance portability library. Many clustering applications are however written in Python and cannot easily benefit from the CLUE algorithm. For this reason making a generalization in k-dimensions of the CLUE algorithm available as a Python library would be very beneficial for the scientific community.
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