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Changelog

Source: NEWS.md

mlr3cluster (development version)

mlr3cluster 0.4.1

Bug fixes

  • clust.agnes, clust.diana, clust.genie, clust.hclust, and clust.protoclust now cut the tree at the current k when predicting, so changing k between training and prediction takes effect.
  • clust.ap no longer errors when affinity propagation finds a single cluster.
  • clust.bico now reclusters the BICO coreset with k-means via stream::DSC_TwoStage(), so k determines the number of predicted clusters instead of returning the raw micro-clusters. Training warns when the coreset is too small to produce k clusters.
  • clust.clara now errors informatively when predicting with metric = "jaccard" or medoids.x = FALSE instead of failing with an obscure upstream error.
  • clust.dbscan_fpc now errors informatively when predicting after training with seeds = FALSE instead of failing with an obscure upstream error.
  • clust.flexmix now trains via flexmix::stepFlexmix(), so nrep actually runs repeated EM initializations and keeps the best fit instead of being silently ignored. Setting nrep together with cluster now errors.
  • clust.genie, clust.kkmeans, and clust.kproto now error informatively during training when the task has fewer than 2 features, which the upstream implementations cannot handle.
  • clust.kkmeans now predicts by assigning observations to the nearest cluster centroid in the kernel-induced feature space, since input-space distances produced wrong assignments for nonlinear kernels. The model is now a list with the fitted object, training data, and per-cluster kernel statistics.
  • clust.movMF now derives the stored $assignments from the predict method, so predicting on the training data yields the training assignments.
  • clust.som now predicts and derives the stored $assignments via kohonen::map(), so models trained with keep.data = FALSE no longer fail at predict or store empty assignments.
  • clust.stdbscan now errors during training when the task does not have exactly 3 features (two spatial coordinates and one temporal coordinate) instead of silently using the wrong columns.
  • clust.tclust no longer exposes the iter.max parameter, which is deprecated in tclust 2.0 in favor of niter1, niter2, and nkeep.
  • clust.wss and clust.entropy now return NaN instead of 0 for empty predictions, which for clust.wss silently skewed aggregated resampling scores.
  • PredictionClust: combined and empty prediction data now retain the PredictionData class, so resample() no longer errors on resampling iterations with an empty test set.
  • PredictionClust: when the partition is derived from a probability matrix, it now uses the cluster labels from the column names instead of the column positions.
  • PredictionClust: empty prob predictions now combine with non-empty ones without error and no longer serialize a spurious prob.V1 column via as.data.table().
  • PredictionClust: as.data.table() no longer drops the partition column for prob-only predictions constructed with check = FALSE, returning NA partitions instead.

mlr3cluster 0.4.0

CRAN release: 2026-06-11

New learners

  • clust.flexmix: Finite mixture model clustering from the flexmix package.
  • clust.genie: Genie hierarchical clustering from the genieclust package.
  • clust.kcca: K-centroids cluster analysis from the flexclust package, supporting k-means, k-medians, spherical, Jaccard, and extended Jaccard families.
  • clust.movMF: Von Mises-Fisher mixture clustering from the movMF package.
  • clust.skmeans: Spherical k-means clustering from the skmeans package.
  • clust.som: Self-organizing maps from the kohonen package.
  • clust.stdbscan: ST-DBSCAN spatio-temporal clustering from the stdbscan package (#83).
  • clust.tclust: Robust trimmed clustering from the tclust package.

New measures

  • clust.avg_between: Average between-cluster distance.
  • clust.avg_within: Average within-cluster distance.
  • clust.davies_bouldin: Davies-Bouldin index.
  • clust.dunn2: Alternative Dunn index using average distances.
  • clust.entropy: Cluster size distribution entropy.
  • clust.pearsongamma: Pearson Gamma correlation between distances and cluster membership.
  • clust.wb_ratio: Within/between distance ratio.

Other improvements

  • Learners no longer store the training data or dissimilarity matrix in the model by default: clust.agnes, clust.diana, clust.fanny, and clust.pam now expose keep.diss and keep.data, clust.clara and clust.kproto expose keep.data, and clust.ap exposes includeSim, all initialized to FALSE. Set the respective parameter to TRUE to restore the previous behavior.
  • Clustering quality measures clust.ch, clust.dunn, and clust.wss are now computed natively instead of relying on fpc::cluster.stats(). The fpc package is no longer a hard dependency.
  • clust.cobweb, clust.em, clust.ff, clust.SimpleKMeans, and clust.xmeans now declare the missings property, since Weka handles missing attribute values natively.
  • clust.diana gains the stop.at.k parameter from cluster::diana().
  • clust.em drops the exclusive property and clust.MBatchKMeans drops fuzzy. Use the prob predict type to select learners with soft memberships.

Bug fixes

  • mlr3cluster is now added to mlr_reflections$loaded_packages to fix errors when using the package in parallel.
  • as_prediction_clust.data.frame() no longer errors with unused argument (with = FALSE) when given a plain data.frame.
  • clust.cmeans now reports a proper error message when an invalid weights value is given instead of failing with a type error.
  • clust.cmeans, clust.kkmeans, and clust.kmeans now accept a matrix of initial cluster centers for the centers parameter, matching the upstream functions.
  • clust.cobweb now declares the hierarchical property instead of partitional, and clust.meanshift declares density instead of partitional.
  • clust.dbscan, clust.dbscan_fpc, clust.hdbscan, and clust.optics now declare the partial property instead of complete, since these algorithms can leave observations unassigned (noise points labeled 0).
  • clust.featureless now returns prob predictions whose most probable cluster matches the predicted partition, with cluster column names consistent with the other learners supporting the prob predict type.
  • clust.silhouette now returns NaN instead of 0 when all observations belong to a single cluster, since the silhouette width is undefined for k < 2.

mlr3cluster 0.3.0

CRAN release: 2026-03-01

  • feat: Add CLARA clustering learner clust.clara from the cluster package.
  • feat: Add k-prototypes clustering learner clust.kproto from the clustMixType package.
  • feat: Add spectral clustering learner clust.specc from the kernlab package.
  • fix: LearnerClustDBSCANfpc now correctly passes the newdata argument in the predict method.
  • fix: LearnerClustKKMeans now correctly passes kernel parameters via the kpar list to kernlab::kkmeans().
  • fix: clust.silhouette measure now has the correct range of [-1, 1].
  • docs: Fix typos in measure documentation.

mlr3cluster 0.2.0

CRAN release: 2026-02-04

  • feat: Mlr3Error and Mlr3Warning classes for errors and warnings.
  • feat: Add protoclust learner from the protoclust package.
  • feat: EM learner now supports probabilistic assignments.
  • fix: Update learner parameter sets to match upstream package changes.
  • docs: Documentation improvements.
  • chore: mlr3cluster now requires R 3.4.0. Following data.table’s minimum R version.
  • chore: mlr3cluster now requires mlr3 (>= 1.3.0) and mlr3misc (>= 0.19.0).

mlr3cluster 0.1.12

CRAN release: 2025-11-19

  • feat: Add cluster_selection_epsilon parameter to HDBSCAN learner and initialize minPts to 5.
  • docs: Better learner example section.

mlr3cluster 0.1.11

CRAN release: 2025-02-18

  • fix: Mclust learner no longer sets the control default with a function not in import to stay compliant with paradox package conventions.

mlr3cluster 0.1.10

CRAN release: 2024-10-03

  • feat: Add BIRCH learner from the stream package.
  • feat: Add BICO learner from the stream package.

mlr3cluster 0.1.9

CRAN release: 2024-03-18

  • feat: Add DBSCAN learner from the fpc package.
  • feat: Add HDBSCAN learner from the dbscan package.
  • feat: Add OPTICS learner from the dbscan package.
  • chore: Compatibility with upcoming paradox release.
  • chore: Move to testthat3.
  • refactor: General code refactoring.

mlr3cluster 0.1.8

CRAN release: 2023-03-12

  • feat: Add new task based on ruspini dataset.

mlr3cluster 0.1.7

CRAN release: 2023-03-10

  • chore: Replace ‘clusterCrit’ measures with alternatives from cluster and fpc packages.
  • fix: Remove broken unloading test.

mlr3cluster 0.1.6

CRAN release: 2022-12-22

  • feat: Add states as row names to usarrests task.
  • fix: Remove dictionary items after unloading package.

mlr3cluster 0.1.5

CRAN release: 2022-11-01

  • feat: Add Mclust learner.
  • fix: Fix error associated with new dbscan release.

mlr3cluster 0.1.4

CRAN release: 2022-08-14

  • refactor: General code refactoring.

mlr3cluster 0.1.3

CRAN release: 2022-04-06

  • feat: Add filter to PredictionClust.
  • fix: Small bug fixes.
  • refactor: General code refactoring.

mlr3cluster 0.1.2

CRAN release: 2021-09-02

  • feat: Add Hclust learner.
  • feat: Add within sum of squares measure.
  • docs: Add tests and documentation for Hclust.
  • docs: Add documentation for WSS measure.
  • refactor: Code factor adaptations.

mlr3cluster 0.1.1

CRAN release: 2020-11-15

  • feat: Add eight new learners.
  • feat: Add assignments and save_assignments fields to LearnerClust class.

mlr3cluster 0.1.0

CRAN release: 2020-10-01

  • Initial upload to CRAN.