J. Schneider and M. Vlachos

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 based on random projections. Our clustering methodology achieves a speedup of two orders of magnitude compared to equivalent state-of-art density-based techniques, while offering analytical guarantees on the clustering quality. We provide a comprehensive analysis of our algorithms and comparison with existing density-based algorithms.

Resources

• Extended version (with all proofs)
• Source code