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Scalable Density-Based Clustering with Quality Guarantees using Random Projections

Reference

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: 3.477)

Publication type

Refereed Journal Article

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.

Persons

Organizational Units

  • Institute of Information Systems
  • Hilti Chair of Business Process Management