In tidyclust: A Common API to Clustering

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r descr_models("db_clust", "hdbscan")

Tuning Parameters

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

param <-
  db_clust() |>
  set_engine("hdbscan") |>
  set_mode("partition") |>
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

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param$item

The hdbscan engine also accepts the engine-specific argument min_cluster_size (passed via set_engine("hdbscan", min_cluster_size = ...)). When supplied, it overrides min_points as the value of minPts passed to dbscan::hdbscan(). If not supplied, min_points is used.

Translation from tidyclust to the original package (partition)

#| label: hdbscan-cls
db_clust(min_points = 5) |>
  set_engine("hdbscan") |>
  set_mode("partition") |>
  translate_tidyclust()

Preprocessing requirements

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What does it mean to predict?

To predict the cluster assignment for a new observation, the nearest training observation that was not classified as noise is found. The new observation is assigned to that neighbor's cluster if the distance is at most the neighbor's core distance; otherwise the new observation is marked as an outlier.

References

Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In Advances in Knowledge Discovery and Data Mining (Vol. 7819, pp. 160–172). Springer. https://link.springer.com/chapter/10.1007/978-3-642-37456-2_14
Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015). Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data, 10(1), 1–51. doi: 10.1145/2733381
Hahsler, M., Piekenbrock, M., & Doran, D. (2019). dbscan: Fast Density-Based Clustering with R. Journal of Statistical Software, 91(1). https://www.jstatsoft.org/article/view/v091i01