DBSCAN Revisited, Revisited

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DBSVEC: Density-Based Clustering Using Support Vector Expansion (2019, methodology): “[17] argue that the original DBSCAN algorithm with a proper configuration performs competitively with ρ-Approximate DBSCAN.”
Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms (2018, background): “…have all been well researched and understood, but there are still many scientiﬁc publications trying to explain these algorithms better (e.g., Schubert et al. 2017), trying to parallelize and scale them to larger data sets (e.g., Lijﬃjt et al. 2015; Yang and Lian 2014), trying to better…”
Deep Learning for Anomaly Detection (2020, methodology): “ge number of studies dedicated to using clustering results to define anomalies,e.g., cluster size [67], distance to cluster centers [60], distance between cluster centers [68], and cluster membership [142]. Gaussian mixture model-based anomaly detection [44, 96] is also included into this category d”