dbscan (version 1.2.2)

hullplot: Plot Clusters

Description

This function produces a two-dimensional scatter plot of data points and colors the data points according to a supplied clustering. Noise points are marked as x. hullplot() also adds convex hulls to clusters.

Usage

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

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).

pch

a vector of plotting characters. By default o is used for points and x for noise points.

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
clplot(x, cl, main = "True clusters")
hullplot(x, cl, main = "True clusters")
### use different 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 = c("grey", "black"), pch = cl)


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