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tidyclust

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The goal of tidyclust is to provide a tidy, unified interface to clustering models. The packages is closely modeled after the parsnip package.

Installation

You can install the released version of tidyclust from CRAN with:

install.packages("tidyclust")

and the development version of tidyclust from GitHub with:

# install.packages("pak")
pak::pak("tidymodels/tidyclust")

Example

The first thing you do is to create a cluster specification. For this example we are creating a K-means model, using the stats engine.

library(tidyclust)
set.seed(1234)

kmeans_spec <- k_means(num_clusters = 3) %>%
  set_engine("stats")

kmeans_spec
#> K Means Cluster Specification (partition)
#> 
#> Main Arguments:
#>   num_clusters = 3
#> 
#> Computational engine: stats

This specification can then be fit using data.

kmeans_spec_fit <- kmeans_spec %>%
  fit(~., data = mtcars)
kmeans_spec_fit
#> tidyclust cluster object
#> 
#> K-means clustering with 3 clusters of sizes 7, 11, 14
#> 
#> Cluster means:
#>        mpg cyl     disp        hp     drat       wt     qsec        vs
#> 1 19.74286   6 183.3143 122.28571 3.585714 3.117143 17.97714 0.5714286
#> 3 26.66364   4 105.1364  82.63636 4.070909 2.285727 19.13727 0.9090909
#> 2 15.10000   8 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000
#>          am     gear     carb
#> 1 0.4285714 3.857143 3.428571
#> 3 0.7272727 4.090909 1.545455
#> 2 0.1428571 3.285714 3.500000
#> 
#> Clustering vector:
#>           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive 
#>                   1                   1                   2                   1 
#>   Hornet Sportabout             Valiant          Duster 360           Merc 240D 
#>                   3                   1                   3                   2 
#>            Merc 230            Merc 280           Merc 280C          Merc 450SE 
#>                   2                   1                   1                   3 
#>          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
#>                   3                   3                   3                   3 
#>   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
#>                   3                   2                   2                   2 
#>       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
#>                   2                   3                   3                   3 
#>    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa 
#>                   3                   2                   2                   2 
#>      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
#>                   3                   1                   3                   2 
#> 
#> Within cluster sum of squares by cluster:
#> [1] 13954.34 11848.37 93643.90
#>  (between_SS / total_SS =  80.8 %)
#> 
#> Available components:
#> 
#> [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
#> [6] "betweenss"    "size"         "iter"         "ifault"

Once you have a fitted tidyclust object, you can do a number of things. predict() returns the cluster a new observation belongs to

predict(kmeans_spec_fit, mtcars[1:4, ])
#> # A tibble: 4 × 1
#>   .pred_cluster
#>   <fct>        
#> 1 Cluster_1    
#> 2 Cluster_1    
#> 3 Cluster_2    
#> 4 Cluster_1

extract_cluster_assignment() returns the cluster assignments of the training observations

extract_cluster_assignment(kmeans_spec_fit)
#> # A tibble: 32 × 1
#>    .cluster 
#>    <fct>    
#>  1 Cluster_1
#>  2 Cluster_1
#>  3 Cluster_2
#>  4 Cluster_1
#>  5 Cluster_3
#>  6 Cluster_1
#>  7 Cluster_3
#>  8 Cluster_2
#>  9 Cluster_2
#> 10 Cluster_1
#> # ℹ 22 more rows

and extract_centroids() returns the locations of the clusters

extract_centroids(kmeans_spec_fit)
#> # A tibble: 3 × 12
#>   .cluster    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>   <fct>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Cluster_1  19.7     6  183. 122.   3.59  3.12  18.0 0.571 0.429  3.86  3.43
#> 2 Cluster_2  26.7     4  105.  82.6  4.07  2.29  19.1 0.909 0.727  4.09  1.55
#> 3 Cluster_3  15.1     8  353. 209.   3.23  4.00  16.8 0     0.143  3.29  3.5

Visual comparison of clustering methods

Below is a visualization of the available models and how they compare using 2 dimensional toy data sets.

Mock comparison for different clustering methods for different data sets. Each row correspods to a clustering method, each column corresponds to a data set type.

Contributing

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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.