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Package website: release | dev
Cluster analysis for mlr3.
mlr3cluster is an extension package for cluster analysis within the mlr3 ecosystem. It is a successor of clustering capabilities of mlr2.
Install the last release from CRAN:
install.packages("mlr3cluster")Install the development version from GitHub:
# install.packages("pak")
pak::pak("mlr-org/mlr3cluster")The current version of mlr3cluster contains:
Also, the package is integrated with mlr3viz which enables you to create great visualizations with just one line of code!
| Key | Label | Packages |
|---|---|---|
| clust.MBatchKMeans | Mini Batch K-Means | ClusterR |
| clust.SimpleKMeans | K-Means (Weka) | RWeka |
| clust.agnes | Agglomerative Hierarchical Clustering | cluster |
| clust.ap | Affinity Propagation Clustering | apcluster |
| clust.cmeans | Fuzzy C-Means Clustering Learner | e1071 |
| clust.cobweb | Cobweb Clustering | RWeka |
| clust.dbscan | Density-Based Clustering | dbscan |
| clust.dbscan_fpc | Density-Based Clustering with fpc | fpc |
| clust.diana | Divisive Hierarchical Clustering | cluster |
| clust.em | Expectation-Maximization Clustering | RWeka |
| clust.fanny | Fuzzy Analysis Clustering | cluster |
| clust.featureless | Featureless Clustering | |
| clust.ff | Farthest First Clustering | RWeka |
| clust.hclust | Agglomerative Hierarchical Clustering | stats |
| clust.hdbscan | HDBSCAN Clustering | dbscan |
| clust.kkmeans | Kernel K-Means | kernlab |
| clust.kmeans | K-Means | stats, clue |
| clust.mclust | Gaussian Mixture Models Clustering | mclust |
| clust.meanshift | Mean Shift Clustering | LPCM |
| clust.optics | OPTICS Clustering | dbscan |
| clust.pam | Partitioning Around Medoids | cluster |
| clust.xmeans | X-means | RWeka |
| Key | Label | Packages |
|---|---|---|
| clust.ch | Calinski Harabasz | fpc |
| clust.dunn | Dunn | fpc |
| clust.silhouette | Silhouette | cluster |
| clust.wss | Within Sum of Squares | fpc |
library(mlr3)
library(mlr3cluster)
task = tsk("usarrests")
learner = lrn("clust.kmeans")
learner$train(task)
prediction = learner$predict(task = task)Check out the blogpost for a more detailed introduction to the package. Also, mlr3book has a section on clustering.
If you have any questions, feedback or ideas, feel free to open an issue here.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.