We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Scalable density-based clustering with quality guarantees using random projections.
- Authors
Schneider, Johannes; Vlachos, Michail
- Abstract
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.
- Subjects
CLUSTER analysis (Statistics); RANDOM projection method; DATA analysis; EUCLIDEAN geometry; PARAMETERS (Statistics)
- Publication
Data Mining & Knowledge Discovery, 2017, Vol 31, Issue 4, p972
- ISSN
1384-5810
- Publication type
Article
- DOI
10.1007/s10618-017-0498-x