Description Usage Arguments Author(s) Examples

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

1 2 |

`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 |

`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. |

`main` |
main title. |

`solid, alpha` |
draw filled polygons instead of just lines for the convex hulls? alpha controls the level of alpha shading. |

`...` |
additional arguments passed on to plot. |

Michael Hahsler

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | ```
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")
``` |

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