dbscan

R package dbscan. A fast reimplementation of several density-based algorithms of the DBSCAN family for spatial data. Includes the DBSCAN (density-based spatial clustering of applications with noise) and OPTICS (ordering points to identify the clustering structure) clustering algorithms and the LOF (local outlier factor) algorithm. The implementations uses the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided.


References in zbMATH (referenced in 13 articles , 1 standard article )

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  1. Li, Yanling; Oravecz, Zita; Zhou, Shuai; Bodovski, Yosef; Barnett, Ian J.; Chi, Guangqing; Zhou, Yuan; Friedman, Naomi P.; Vrieze, Scott I.; Chow, Sy-Miin: Bayesian forecasting with a regime-switching zero-inflated multilevel Poisson regression model: an application to adolescent alcohol use with spatial covariates (2022)
  2. Bertsimas, Dimitris; Orfanoudaki, Agni; Wiberg, Holly: Interpretable clustering: an optimization approach (2021)
  3. Brécheteau, Claire; Fischer, Aurélie; Levrard, Clément: Robust Bregman clustering (2021)
  4. Michael C. Thrun, Quirin Stier: Fundamental clustering algorithms suite (2021) not zbMATH
  5. Vouros, Avgoustinos; Langdell, Stephen; Croucher, Mike; Vasilaki, Eleni: An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations (2021)
  6. Almodóvar-Rivera, Israel A.; Maitra, Ranjan: Kernel-estimated nonparametric overlap-based syncytial clustering (2020)
  7. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  8. Kandanaarachchi, Sevvandi; Muñoz, Mario A.; Hyndman, Rob J.; Smith-Miles, Kate: On normalization and algorithm selection for unsupervised outlier detection (2020)
  9. Ni, Yang; Müller, Peter; Diesendruck, Maurice; Williamson, Sinead; Zhu, Yitan; Ji, Yuan: Scalable Bayesian nonparametric clustering and classification (2020)
  10. Ferraro, Maria Brigida; Giordani, Paolo: A review and proposal of (fuzzy) clustering for nonlinearly separable data (2019)
  11. Michael Hahsler; Matthew Piekenbrock; Derek Doran: dbscan: Fast Density-Based Clustering with R (2019) not zbMATH
  12. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  13. Michael Hahsler and Matthew Bolaños and John Forrest: Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017) not zbMATH