tidyclust - expanding tidymodels to clustering
summary
A new package called tidyclust adds clustering to tidymodels by letting users create clustering specs (k‑means), plug recipes (normalization, PCA) into workflows, and fit without outcomes; centroids come back in the recipe’s transformed space and point assignments appear as a .cluster tibble. It provides extract_centroids, cluster metrics (total within‑sum‑of‑squares) and a tuner tune_cluster to search num_clusters (example grid 1–10). Early development but the pipeline and diagnostics already run end to end.
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chapters (9)
- 0:00Introduction to tidymodels
- 2:25Introducing tidycluster
- 4:17Challenges in clustering
- 7:14Example data overview
- 9:00Clustering workflow
- 11:59Extracting model results
- 15:10Clustering metrics
- 16:12Tuning clustering models
- 18:17Conclusion and next steps
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