Description
Cluster Extension for 'mlr3'.
Description
Extends the 'mlr3' package with cluster analysis.
README.md
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.