Estimate the Average Jaccard Index for Cluster Stability

This function estimates the stability of cluster assignments by calculating the average Jaccard index: how well do cluster assignments of new data from a pre-trained model align with cluster assignments of the same data from a new model fitted on the same data (Hennig, 2007).

Usage

jaccard_avg(object, ...)

# S3 method for class 'cluster_spec'
jaccard_avg(object, ...)

# S3 method for class 'cluster_fit'
jaccard_avg(object, new_data = NULL, ...)

# S3 method for class 'workflow'
jaccard_avg(object, new_data = NULL, ...)

Arguments

object

A fitted tidyclust model

...

Not currently used.

new_data

A dataset to predict on.

Value

A tibble with 3 columns; .metric, .estimator, and .estimate.

Details

Note that this metric will re-fit the model on the new dataset (new_data), and so might be computationally expensive.

References

Hennig, C. (2007). Cluster-wise assessment of cluster stability. Computational Statistics & Data Analysis, 52(1), 258-271.

See also

fpc::clusterboot()

Other cluster metric: pred_strength_min()

Other ML4Psych: metric_by_event(), pred_strength_min()

Examples

library(tidymodels)
library(tidyclust)

penguins <- drop_na(penguins)

kmeans_spec <- k_means(num_clusters = tune())

res <- tune_cluster(
  kmeans_spec,
  ~ bill_length_mm + bill_depth_mm + flipper_length_mm + body_mass_g,
  resamples = bootstraps(penguins, times = 10),
  metrics = cluster_metric_set(jaccard_avg)
)

# What's the largest k that has high recovery rate?
# (values above 0.75 are considered good, values below 0.5 are considered bad)
autoplot(res)