Scalable density-based clustering with quality guarantees using random projections
| Autor: | Johannes Schneider, Michail Vlachos |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
DBSCAN
Fuzzy clustering Computer Networks and Communications Computer science Correlation clustering Constrained clustering 02 engineering and technology computer.software_genre Computer Science Applications Data stream clustering CURE data clustering algorithm 020204 information systems 0202 electrical engineering electronic engineering information engineering Canopy clustering algorithm 020201 artificial intelligence & image processing Data mining Cluster analysis computer Information Systems |
| Zdroj: | Data Mining and Knowledge Discovery. 31:972-1005 |
| ISSN: | 1573-756X 1384-5810 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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