Customized Cross-Entropy Clustering

This function performs cross-entropy clustering on a data matrix. It is based on cec but is limited to 2D matrices and implements its own splitting process.

kbox(
  x,
  centers = 1,
  iter.max = 10,
  split = FALSE,
  split.width = Inf,
  split.height = Inf,
  split.density = 0,
  min.size = 0,
  split.sensitivity = 0
)

Arguments

x

A numeric matrix with two columns.

centers

Either a matrix of initial centers or the number of initial centers.

iter.max

Maximum number of iterations at each clustering.

split

Enables split mode. This mode discovers new clusters after initial clustering, by trying to split single clusters into two.

split.width

The maximum authorized width of a cluster. If a cluster is wider than split.width, the function will attempt to split it in two.

split.height

The maximum authorized height of a cluster. If a cluster is higher than split.height, the function will attempt to split it in two.

split.density

The minimum authorized density of a cluster. If a cluster is less dense than split.density, the function will attempt to split it in two.

min.size

The minimum authorized size (in number of items) of a cluster. If a cluster is smaller than min.size, the function will attempt to split it in two.

split.sensitivity

The minimum amount of improvement in the cost function of the cross-entropy clustering for a splitting event to be considered valid.

Value

A matrix with 6 columns: x and y coordinates of the centers of the clusters, width, height, and angle of the covariance ellipse best describing each cluster, and the number of element in each cluster.

Author

Simon Garnier, garnier@njit.edu

Examples

x <- c(rnorm(25, 4), rnorm(25, -2))
y <- c(rnorm(25, 2), rnorm(25, -3))
k <- kbox(cbind(x, y), 2)
plot(x, y, asp = 1)
apply(k, 1, function(k) {
  lines(ellipse(k[1], k[2], k[3], k[4], k[5]))
})

#> NULL