dbscan (version 1.1-11)

hullplot: 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

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")