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Turn a dbscan clustering object into a tidy tibble

Source: R/broom-dbscan-tidiers.R
dbscan_tidiers.Rd

Provides tidy(), augment(), and glance() verbs for clusterings created with algorithms in package dbscan to work with tidymodels.

Usage

tidy(x, ...)

# S3 method for class 'dbscan'
tidy(x, ...)

# S3 method for class 'hdbscan'
tidy(x, ...)

# S3 method for class 'general_clustering'
tidy(x, ...)

augment(x, ...)

# S3 method for class 'dbscan'
augment(x, data = NULL, newdata = NULL, ...)

# S3 method for class 'hdbscan'
augment(x, data = NULL, newdata = NULL, ...)

# S3 method for class 'general_clustering'
augment(x, data = NULL, newdata = NULL, ...)

glance(x, ...)

# S3 method for class 'dbscan'
glance(x, ...)

# S3 method for class 'hdbscan'
glance(x, ...)

# S3 method for class 'general_clustering'
glance(x, ...)

Arguments

x

A clustering object returned by dbscan(), hdbscan(), jpclust() or sNNclust().

...

further arguments are ignored without a warning.

data

The data used to create the clustering.

newdata

New data to predict cluster labels for.

Value

A tibble (see Details for the columns).

Details

Methods are available for the clustering objects created by dbscan(), hdbscan(), jpclust() and sNNclust(). Clusterings created by optics() are not supported.

All three verbs return a tibble. Cluster 0 represents noise points. Noise points are not assigned to a cluster.

tidy()

Returns one row per cluster, including the noise cluster 0, with the columns:

  • cluster: the cluster label as a factor (the levels are 0 to the number of clusters).

  • size: the number of points in the cluster.

  • noise: TRUE for cluster 0 (the noise points).

  • cluster_score (only for hdbscan()): the stability score of the cluster (see the component cluster_scores in hdbscan()). It is NA for the noise cluster.

augment()

Returns the data (or newdata) as a tibble with additional columns. The original data used to create the clustering always needs to be passed as data because the clustering object does not store it. If newdata is specified, then the cluster labels of the new points are predicted with predict(). Otherwise, the labels in the clustering are used. The added columns are:

  • .cluster: the cluster label as a factor.

  • noise (only for dbscan()): TRUE if the point is a noise point.

  • .coredist, .membership_prob and .outlier_scores (only for hdbscan()): the core distance, the cluster membership probability and the GLOSH outlier score of each point (see hdbscan()). They are NA for points in newdata.

Augmenting newdata is not supported for jpclust() and sNNclust().

glance()

Returns a one-row summary of the clustering with the columns:

  • nobs: the number of points.

  • n.clusters: the number of clusters (without noise).

  • nexcluded: the number of noise points.

See also

generics::tidy(), generics::augment(), generics::glance(), dbscan()

Examples


data(iris)
x <- scale(iris[, 1:4])

## dbscan
db <- dbscan(x, eps = .9, minPts = 5)
db
#> DBSCAN clustering for 150 objects.
#> Parameters: eps = 0.9, minPts = 5
#> Using euclidean distances and borderpoints = TRUE
#> The clustering contains 2 cluster(s) and 4 noise points.
#> 
#>  0  1  2 
#>  4 49 97 
#> 
#> Available fields: cluster, eps, minPts, metric, borderPoints

# summarize model fit with tidiers
tidy(db)
#> # A tibble: 3 × 3
#>   cluster  size noise
#>   <fct>   <int> <lgl>
#> 1 0           4 TRUE 
#> 2 1          49 FALSE
#> 3 2          97 FALSE
glance(db)
#> # A tibble: 1 × 3
#>    nobs n.clusters nexcluded
#>   <int>      <int>     <int>
#> 1   150          2         4

# augment for this model needs the original data
augment(db, x)
#> # A tibble: 150 × 6
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width .cluster noise
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>    <lgl>
#>  1       -0.898      1.02          -1.34       -1.31 1        FALSE
#>  2       -1.14      -0.132         -1.34       -1.31 1        FALSE
#>  3       -1.38       0.327         -1.39       -1.31 1        FALSE
#>  4       -1.50       0.0979        -1.28       -1.31 1        FALSE
#>  5       -1.02       1.25          -1.34       -1.31 1        FALSE
#>  6       -0.535      1.93          -1.17       -1.05 1        FALSE
#>  7       -1.50       0.786         -1.34       -1.18 1        FALSE
#>  8       -1.02       0.786         -1.28       -1.31 1        FALSE
#>  9       -1.74      -0.361         -1.34       -1.31 1        FALSE
#> 10       -1.14       0.0979        -1.28       -1.44 1        FALSE
#> # ℹ 140 more rows

# to augment new data, the original data is also needed
augment(db, x, newdata = x[1:5, ])
#> # A tibble: 5 × 6
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width .cluster noise
#>          <dbl>       <dbl>        <dbl>       <dbl> <fct>    <lgl>
#> 1       -0.898      1.02          -1.34       -1.31 1        FALSE
#> 2       -1.14      -0.132         -1.34       -1.31 1        FALSE
#> 3       -1.38       0.327         -1.39       -1.31 1        FALSE
#> 4       -1.50       0.0979        -1.28       -1.31 1        FALSE
#> 5       -1.02       1.25          -1.34       -1.31 1        FALSE

## hdbscan
hdb <- hdbscan(x, minPts = 5)

# summarize model fit with tidiers
tidy(hdb)
#> # A tibble: 3 × 4
#>   cluster  size cluster_score noise
#>   <fct>   <int>         <dbl> <lgl>
#> 1 0           2          NA   TRUE 
#> 2 1          98         118.  FALSE
#> 3 2          50          87.4 FALSE
glance(hdb)
#> # A tibble: 1 × 3
#>    nobs n.clusters nexcluded
#>   <int>      <int>     <int>
#> 1   150          2         2

# augment for this model needs the original data
augment(hdb, x)
#> # A tibble: 150 × 8
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width .cluster .coredist
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>        <dbl>
#>  1       -0.898      1.02          -1.34       -1.31 2            0.236
#>  2       -1.14      -0.132         -1.34       -1.31 2            0.236
#>  3       -1.38       0.327         -1.39       -1.31 2            0.310
#>  4       -1.50       0.0979        -1.28       -1.31 2            0.283
#>  5       -1.02       1.25          -1.34       -1.31 2            0.291
#>  6       -0.535      1.93          -1.17       -1.05 2            0.463
#>  7       -1.50       0.786         -1.34       -1.18 2            0.496
#>  8       -1.02       0.786         -1.28       -1.31 2            0.248
#>  9       -1.74      -0.361         -1.34       -1.31 2            0.551
#> 10       -1.14       0.0979        -1.28       -1.44 2            0.270
#> # ℹ 140 more rows
#> # ℹ 2 more variables: .membership_prob <dbl>, .outlier_scores <dbl>

# to augment new data, the original data is also needed
augment(hdb, x, newdata = x[1:5, ])
#> # A tibble: 5 × 8
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width .cluster .coredist
#>          <dbl>       <dbl>        <dbl>       <dbl> <fct>        <dbl>
#> 1       -0.898      1.02          -1.34       -1.31 2               NA
#> 2       -1.14      -0.132         -1.34       -1.31 2               NA
#> 3       -1.38       0.327         -1.39       -1.31 2               NA
#> 4       -1.50       0.0979        -1.28       -1.31 2               NA
#> 5       -1.02       1.25          -1.34       -1.31 2               NA
#> # ℹ 2 more variables: .membership_prob <dbl>, .outlier_scores <dbl>

## Jarvis-Patrick clustering
cl <- jpclust(x, k = 20, kt = 15)

# summarize model fit with tidiers
tidy(cl)
#> # A tibble: 7 × 3
#>   cluster  size noise
#>   <fct>   <int> <lgl>
#> 1 0           0 TRUE 
#> 2 1          49 FALSE
#> 3 2           1 FALSE
#> 4 3          13 FALSE
#> 5 4          36 FALSE
#> 6 5           2 FALSE
#> 7 6          49 FALSE
glance(cl)
#> # A tibble: 1 × 3
#>    nobs n.clusters nexcluded
#>   <int>      <int>     <int>
#> 1   150          6         0

# augment for this model needs the original data
augment(cl, x)
#> # A tibble: 150 × 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width .cluster
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>   
#>  1       -0.898      1.02          -1.34       -1.31 1       
#>  2       -1.14      -0.132         -1.34       -1.31 1       
#>  3       -1.38       0.327         -1.39       -1.31 1       
#>  4       -1.50       0.0979        -1.28       -1.31 1       
#>  5       -1.02       1.25          -1.34       -1.31 1       
#>  6       -0.535      1.93          -1.17       -1.05 1       
#>  7       -1.50       0.786         -1.34       -1.18 1       
#>  8       -1.02       0.786         -1.28       -1.31 1       
#>  9       -1.74      -0.361         -1.34       -1.31 1       
#> 10       -1.14       0.0979        -1.28       -1.44 1       
#> # ℹ 140 more rows

## Shared Nearest Neighbor clustering
cl <- sNNclust(x, k = 20, eps = 0.8, minPts = 15)

# summarize model fit with tidiers
tidy(cl)
#> # A tibble: 3 × 3
#>   cluster  size noise
#>   <fct>   <int> <lgl>
#> 1 0          10 TRUE 
#> 2 1          45 FALSE
#> 3 2          95 FALSE
glance(cl)
#> # A tibble: 1 × 3
#>    nobs n.clusters nexcluded
#>   <int>      <int>     <int>
#> 1   150          2        10

# augment for this model needs the original data
augment(cl, x)
#> # A tibble: 150 × 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width .cluster
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>   
#>  1       -0.898      1.02          -1.34       -1.31 1       
#>  2       -1.14      -0.132         -1.34       -1.31 1       
#>  3       -1.38       0.327         -1.39       -1.31 1       
#>  4       -1.50       0.0979        -1.28       -1.31 1       
#>  5       -1.02       1.25          -1.34       -1.31 1       
#>  6       -0.535      1.93          -1.17       -1.05 1       
#>  7       -1.50       0.786         -1.34       -1.18 1       
#>  8       -1.02       0.786         -1.28       -1.31 1       
#>  9       -1.74      -0.361         -1.34       -1.31 1       
#> 10       -1.14       0.0979        -1.28       -1.44 1       
#> # ℹ 140 more rows