mlr3cluster
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.
Installation
Install the last release from CRAN:
install.packages("mlr3cluster")Install the development version from GitHub:
devtools::install_github("mlr-org/mlr3cluster")Feature Overview
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!
Cluster Analysis
Cluster Learners
| ID | Learner | Package |
|---|---|---|
| clust.agnes | Agglomerative Hierarchical Clustering | cluster |
| clust.ap | Affinity Propagation Clustering | apcluster |
| clust.cmeans | Fuzzy C-Means Clustering | e1071 |
| clust.cobweb | Cobweb Clustering Algorithm | 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 Clustering | cluster |
| clust.featureless | Simple Featureless Clustering | mlr3cluster |
| clust.ff | FarthestFirst Clustering Algorithm | RWeka |
| clust.hdbscan | HDBSCAN Clustering | dbscan |
| clust.hclust | Agglomerative Hierarchical Clustering | stats |
| clust.kkmeans | Kernel K-Means Clustering | kernlab |
| clust.kmeans | K-Means Clustering | stats |
| clust.mclust | Gaussian Mixture Models-Based Clustering | mclust |
| clust.MBatchKMeans | Mini Batch K-Means Clustering | ClusterR |
| clust.meanshift | Mean Shift Clustering | LPCM |
| clust.optics | OPTICS Clustering | dbscan |
| clust.pam | Clustering Around Medoids | cluster |
| clust.SimpleKMeans | K-Means Clustering (WEKA) | RWeka |
| clust.xmeans | K-Means with Automatic Determination of k | RWeka |
Cluster Measures
| ID | Measure | Package |
|---|---|---|
| clust.dunn | Dunn index | fpc |
| clust.ch | Calinski Harabasz Pseudo F-Statistic | fpc |
| clust.silhouette | Rousseeuw's Silhouette Quality Index | cluster |
| clust.wss | Within Sum of Squares | fpc |
Example
library(mlr3)
library(mlr3cluster)
task = tsk("usarrests")
learner = lrn("clust.kmeans")
learner$train(task)
preds = learner$predict(task = task)More Resources
Check out the blogpost for a more detailed introduction to the package. Also, mlr3book has a section on clustering.
Future Plans
- 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.