Scalable Density-Based Clustering with Quality Guarantees using Random Projections

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Schneider, J., & Vlachos, M. (2017). Scalable Density-Based Clustering with Quality Guarantees using Random Projections. Data Mining and Knowledge Discovery (DMKD), 14(1), 85-96. (ISI_2016: 3.16; ISI_2016_5year: 3.477; ISI_2018: 2.879)


Beitrag in wissenschaftlicher Fachzeitschrift


Clustering offers signi?cant 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 scal- able density-based clustering algorithms using random projections. Our clus- tering methodology achieves a speedup of two orders of magnitude compared with equivalent state-of-art density-based techniques, while o?ering 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.



  • Institut für Wirtschaftsinformatik
  • Hilti Lehrstuhl für Business Process Management

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