dbscan_tidiers: Turn an dbscan clustering object into a tidy tibble

In dbscan: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms

Turn an dbscan clustering object into a tidy tibble

Description

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	An dbscan object returned from dbscan().
...	further arguments are ignored without a warning.
data	The data used to create the clustering.
newdata	New data to predict cluster labels for.

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

# summarize model fit with tidiers
tidy(db)
glance(db)

# augment for this model needs the original data
augment(db, x)

# to augment new data, the original data is also needed
augment(db, x, newdata = x[1:5, ])

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

# summarize model fit with tidiers
tidy(hdb)
glance(hdb)

# augment for this model needs the original data
augment(hdb, x)

# to augment new data, the original data is also needed
augment(hdb, x, newdata = x[1:5, ])

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

# summarize model fit with tidiers
tidy(cl)
glance(cl)

# augment for this model needs the original data
augment(cl, x)

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

# summarize model fit with tidiers
tidy(cl)
glance(cl)

# augment for this model needs the original data
augment(cl, x)

dbscan documentation built on June 29, 2024, 1:07 a.m.