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.