hullplot: Plot Convex Hulls of Clusters

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

Plot Convex Hulls of Clusters

Description

This function produces a two-dimensional scatter plot with added convex hulls for clusters.

Usage

hullplot(
  x,
  cl,
  col = NULL,
  cex = 0.5,
  hull_lwd = 1,
  hull_lty = 1,
  solid = TRUE,
  alpha = 0.2,
  main = "Convex Cluster Hulls",
  ...
)

Arguments

x	a data matrix. If more than 2 columns are provided, then the data is plotted using the first two principal components.
cl	a clustering. Either a numeric cluster assignment vector or a clustering object (a list with an element named cluster).
col	colors used for clusters. Defaults to the standard palette. The first color (default is black) is used for noise/unassigned points (cluster id 0).
cex	expansion factor for symbols.
hull_lwd, hull_lty	line width and line type used for the convex hull.
solid, alpha	draw filled polygons instead of just lines for the convex hulls? alpha controls the level of alpha shading.
main	main title.
...	additional arguments passed on to plot.

Author(s)

Michael Hahsler

Examples

set.seed(2)
n <- 400

x <- cbind(
  x = runif(4, 0, 1) + rnorm(n, sd = 0.1),
  y = runif(4, 0, 1) + rnorm(n, sd = 0.1)
  )
cl <- rep(1:4, time = 100)

### original data with true clustering
hullplot(x, cl, main = "True clusters")
### use differnt symbols
hullplot(x, cl, main = "True clusters", pch = cl)
### just the hulls
hullplot(x, cl, main = "True clusters", pch = NA)
### a version suitable for b/w printing)
hullplot(x, cl, main = "True clusters", solid = FALSE, col = "black", pch = cl)


### run some clustering algorithms and plot the resutls
db <- dbscan(x, eps = .07, minPts = 10)
hullplot(x, db, main = "DBSCAN")

op <- optics(x, eps = 10, minPts = 10)
opDBSCAN <- extractDBSCAN(op, eps_cl = .07)
hullplot(x, opDBSCAN, main = "OPTICS")

opXi <- extractXi(op, xi = 0.05)
hullplot(x, opXi, main = "OPTICSXi")

# Extract minimal 'flat' clusters only
opXi <- extractXi(op, xi = 0.05, minimum = TRUE)
hullplot(x, opXi, main = "OPTICSXi")

km <- kmeans(x, centers = 4)
hullplot(x, km, main = "k-means")

hc <- cutree(hclust(dist(x)), k = 4)
hullplot(x, hc, main = "Hierarchical Clustering")

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