Package 'leaderCluster'

Calculate clusters using Hartigan's Leader Algorithm

Description

Takes a matrix of coordinates and outputs cluster ids from running the leader algorithm. The coordinates can either be on points in the space R^n, or latitude/longitude pairs. A radius delta must be provided.

Usage

leaderCluster(
  points,
  radius,
  weights = rep(1, nrow(points)),
  max_iter = 10L,
  distance = c("Lp", "L1", "L2", "Linf", "haversine"),
  p = 2
)

Arguments

points	A matrix, or something which can be coerced into a matrix, of coordinates with rows representing points and columns representing dimensions. If using haversine distance, this must be a two column matrix with the first column containing latitudes in decimal degrees and the second containing longitudes in decimal degrees.
radius	A scalar value giving the radius of the resulting clusters; this is the main tuning parameter for the algorithm. When using the haversine distance this value should be in kilometres.
weights	An vector of weights, one per row of points, to apply to the clustering algorithm.
max_iter	Maximum number of times to iterate the algorithm; can safely set to 1 in many instances. See Details.
distance	The method to be used for calculating distances between points. If this is set to haversine, the points must be a two column matrix. If Lp, then the p input specifies which norm is being used.
p	When using Lp as the value for distance, this is a positive number specifing which Lp-norm to implement. For p equal to 1,2, or Inf, a special implementation will be used which is slightly more efficent than the more general application.

Details

The value for delta defines an approximate radius of each cluster. As the algorithm runs, a point within a distance delta from the centroid of a cluster will be labeled with the coorisponding cluster. As centroid clusters move, it is possible for the final radius of each cluster to be slightly larger than delta.

Unlike many other iterative clustering algorithms, the leader algorithm typically provides reasonable clusters after just a single pass. When speed is of concern, the max_iter value may be safely set to 1. However, the algorithm typically fully converges in only a few cycles; also, a convergent solution will usually have a smaller number of clusters than a solution with only one pass.

The algorithm scales nicely, and can fit a model with 100s of columns and 100k's of rows in (on a relatively modest machine) under a minute. However, the processing time decays significantly if the radius is too small, since the number of clusters will be very high.

Value

A list containing a vector of cluster ids, a matrix of cluster centroids, the number of clusters, and the number iterations.

Author(s)

Taylor B. Arnold, [email protected]

References

J. A. Hartigan. Clustering Algorithms. John Wiley & Sons, New York, 1975.

Examples

points <- 1:10
out <- leaderCluster(points, radius=2, distance="Lp", max_iter=1L)

par(mar = c(0,0,0,0), mfrow = c(1,3))
set.seed(1)
points <- matrix(runif(100*2), ncol=2)
for(r in c(0.1, 0.2, 0.4)) {
 out <- leaderCluster(points = points, radius = r, distance="L2")$cluster_id
 cols <- rainbow(length(unique(out)))[out]
 plot(points, pch = 19, cex = 0.7, col = cols, axes = FALSE)
 points(points[!duplicated(out),,drop=FALSE], cex = 2, col = unique(cols))
 box()
}

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