Referenz
Schneider, J., & Vlachos, M. (2017). Scalable Density-Based Clustering with Quality Guarantees using Random Projections. Data Mining and Knowledge Discovery (DMKD), 31(4), 972-1005.
Publikationsart
Beitrag in wissenschaftlicher Fachzeitschrift
Abstract
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.
Mitarbeiter
Einrichtungen
- Institut für Wirtschaftsinformatik
- Hilti Lehrstuhl für Business Process Management