Scalable density-based clustering with quality guarantees using random projections

• Autores: Johannes Schneider, Michail Vlachos
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 31, Nº 4, 2017, págs. 972-1005
• Idioma: inglés
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• Resumen

  • 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.