mlr3cluster 0.4.0

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

• 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

• 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

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

mlr3cluster 0.1.11

• 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

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

mlr3cluster 0.1.9

• 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

• feat: Add new task based on ruspini dataset.

mlr3cluster 0.1.7

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

mlr3cluster 0.1.6

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

mlr3cluster 0.1.5

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

mlr3cluster 0.1.4

• refactor: General code refactoring.

mlr3cluster 0.1.3

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

mlr3cluster 0.1.2

• 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

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

mlr3cluster 0.1.0

• Initial upload to CRAN.