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:
- A selection of 22 clustering learners that represent a wide variety of clusterers: partitional, hierarchical, fuzzy, etc.
- A selection of 4 performance measures
- Two built-in tasks to get started with clustering
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.
- Add more learners and measures
- Integrate the package with mlr3pipelines (work in progress)
If you have any questions, feedback or ideas, feel free to open an issue here.