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Changelog

Source: NEWS.md

mlr3cluster (development version)

mlr3cluster 0.5.0

CRAN release: 2026-08-21

New learners

  • clust.gmeans: G-means clustering from the gmeans package, which extends k-means by automatically determining the number of clusters.
  • clust.kmeans_rcpp: K-means clustering via ClusterR::KMeans_rcpp() from the ClusterR package.
  • clust.kmodes: K-modes clustering for categorical data from the klaR package.

New measures

  • clust.sse_ratio: Within-cluster sum of squares divided by total sum of squares, a normalized measure of cluster compactness.

Other improvements

  • clust.cobweb now exposes the output_debug_info parameter, matching the other RWeka-based learners.
  • clust.cobweb, clust.em, clust.ff, and clust.SimpleKMeans now support marshaling.
  • clust.kcca now exposes simulated annealing through classify = "simann" and the simann parameter.
  • clust.meanshift now predicts on new data. It runs mean shift from each observation with the bandwidth and convergence threshold used for training, then assigns the observation to the mode it reaches. Previously, prediction warned and returned the training partition.
  • clust.movMF now exposes the ids parameter, which initializes the EM algorithm from fixed component memberships.
  • clust.protoclust now predicts on new data by assigning each observation to the cluster with the nearest prototype. Previously, prediction warned and returned the training partition. Its model now stores the fitted object and training data in a list.
  • clust.skmeans now exposes start for selecting the initialization and maxchains for the "pclust" and "LIHC" methods.
  • clust.stdbscan now supports observation weights through weights, matching clust.dbscan.

Bug fixes

  • Clustering measures now handle character features consistently with factor and ordered features. Distance-based measures use Gower distances instead of treating character values as missing, while measures that require numeric features report an informative error.
  • as_prediction_clust() now converts whole-numbered partition values to integers instead of rejecting them.
  • clust.agnes, clust.diana, clust.genie, and clust.hclust now validate k against the number of training observations when predicting. They previously used the number of rows in the new data even though cutree() cuts the training tree.
  • clust.ap now breaks similarity ties deterministically when predicting, matching the assignment rule of the apcluster package.
  • clust.ap no longer fails to train on tasks with a feature named m.
  • clust.bico and clust.kcca now require k >= 2, matching their existing training requirements. Invalid values now fail when set rather than during training.
  • clust.clara and clust.pam now reject prediction when trained with stand = TRUE, because clue::cl_predict() would otherwise assign clusters using unstandardized new data.
  • clust.cmeans now returns a plain probability matrix without clue’s internal attributes.
  • clust.cobweb, clust.ff, and clust.xmeans now allow the seed S = 0, which Weka accepts, matching the seed bound of clust.em and clust.SimpleKMeans.
  • clust.dbscan_fpc now rejects prediction when trained with scale = TRUE. This prevents comparisons between unscaled new data and eps, which applies to the scaled training data.
  • clust.hclust and clust.protoclust now require the Minkowski power p to be greater than 0, matching stats::dist().
  • clust.hdbscan now requires minPts >= 2, matching dbscan::hdbscan(). Smaller values now fail when set rather than during training.
  • clust.kcca now validates initcent as a single string and documents its default as "randomcent". Invalid values now fail when set rather than producing an obscure S4 validity error during training.
  • clust.kcca no longer exposes the ntry and min.size parameters, which are flexclust::qtclust() control options that flexclust::kcca() ignores.
  • clust.kkmeans now resolves prediction distance ties with "first" instead of "random", so predictions are reproducible without setting a seed.
  • clust.mclust now stores $assignments as an integer vector, consistent with the other learners, instead of the double vector returned by mclust.
  • clust.optics, clust.som, and clust.tclust now coerce logical features to numeric, so tasks with only logical features train and predict instead of erroring in the upstream packages.
  • clust.SimpleKMeans now declares min_density as a nonnegative double instead of a positive integer, since Weka accepts fractional canopy densities.
  • PredictionClust now preserves measure weights from tasks with a weights_measure column in $weights and as.data.table() instead of dropping them.
  • PredictionClust now errors during construction when a partition label has no matching prob column, instead of silently accepting inconsistent predictions.

mlr3cluster 0.4.1

CRAN release: 2026-07-10

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.