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The density-based clustering algorithm DBSCAN is a state-of-the-art data clustering technique with numerous applications in many fields. However, DBSCAN requires neighborhood queries for all objects and propagation of labels from object to object. This scheme is time consuming and thus limits its applicability for large datasets. In this paper, we propose a novel anytime approach to cope with this...
The structural graph clustering algorithm SCAN is a fundamental technique for managing and analyzing graph data. However, its high runtime remains a computational bottleneck, which limits its applicability. In this paper, we propose a novel interactive approach for tackling this problem on multicore CPUs. Our algorithm, called anySCAN, iteratively processes vertices in blocks. The acquired results...
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