plot_kNNdist {evprof}

Plot kNNdist

Description

Plot the kNN (k-nearest neighbors) distance plot to visually detect the "elbow" and define an appropriate value for eps DBSCAN parameter.

Usage

plot_kNNdist(
  sessions,
  MinPts = NULL,
  log = FALSE,
  start = getOption("evprof.start.hour")
)

Arguments

sessions

tibble, sessions data set in evprof standard format.

MinPts

integer, DBSCAN MinPts parameter. If null, a value of 200 will be considered.

log

logical, whether to transform ConnectionStartDateTime and ConnectionHours variables to natural logarithmic scale (base = exp(1)).

start

integer, start hour in the x axis of the plot.

Details

The kNN (k-nearest neighbors) distance plot can provide insights into setting the eps parameter in DBSCAN. The "elbow" in the kNN distance plot is the point where the distances start to increase significantly. At the same time, for DBSCAN, the eps parameter defines the radius within which a specified number of points must exist for a data point to be considered a core point. Therefore, the "elbow" of the kNN distance plot can provide a sense of the scale of the data and help you choose a reasonable range for the eps parameter in DBSCAN.

Value

plot

Examples

library(dplyr)
california_ev_sessions %>%
  sample_frac(0.05) %>%
  plot_kNNdist(start = 3, log = TRUE)