Kubilay Atasu, Thomas Parnell, et al.
ICPP 2017
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.
Kubilay Atasu, Thomas Parnell, et al.
ICPP 2017
Kubilay Atasu, Thomas Parnell, et al.
Big Data 2017
Reinhard Heckel, Michalis Vlachos
SDM 2017
Michalis Vlachos, Nikolaos M. Freris, et al.
VLDB Journal