Johannes Schneider, Michael Elkin, et al.
Theoretical Computer Science
Clustering offers significant insights in data analysis. Density-based algorithms have emerged as flexible and efficient techniques, able to discover high-quality and potentially irregularly shaped clusters. Here, we present scalable density-based clustering algorithms using random projections. Our clustering methodology achieves a speedup of two orders of magnitude compared with equivalent state-of-art density-based techniques, while offering analytical guarantees on the clustering quality in Euclidean space. Moreover, it does not introduce difficult to set parameters. We provide a comprehensive analysis of our algorithms and comparison with existing density-based algorithms.
Johannes Schneider, Michael Elkin, et al.
Theoretical Computer Science
Michalis Vlachos, Johannes Schneider, et al.
ACM TKDD
Johannes Schneider, Michalis Vlachos
SDM 2018
Francesco Fusco, Michalis Vlachos, et al.
IJCAI 2019