In tidyclust: A Common API to Clustering

r descr_models("db_clust", "dbscan")

Tuning Parameters

defaults <- 
  tibble::tibble(tidyclust = c("radius", "min_points"),
                 default = c("no default", "no_default"))

param <-
 db_clust() %>% 
  set_engine("dbscan") %>% 
  set_mode("partition") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

Translation from tidyclust to the original package (partition)

db_clust(radius = 0.5, min_points = 5)%>% 
  set_engine("dbscan") %>% 
  set_mode("partition") %>% 
  translate_tidyclust()

Preprocessing requirements

References

• Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.

• Hahsler, M., Piekenbrock, M., & Doran, D. (2019a). Dbscan : Fast Density-Based Clustering with r. Journal of Statistical Software, 91(1). https://www.jstatsoft.org/article/view/v091i01

• Kriegel, H., Kröger, P., Sander, J., & Zimek, A. (2011). Density-based clustering. WIREs Data Mining and Knowledge Discovery, 1(3), 231–240. doi: 10.1002/widm.30. 30

• Tran, T. N., Drab, K., & Daszykowski, M. (2013). Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemometrics and Intelligent Laboratory Systems, 49 120, 92–96. doi: 10.1016/j.chemolab.2012.11.006

tidyclust documentation built on June 20, 2026, 9:08 a.m.