Several of the methods I use for analyzing large data sets, such as
WinGamma: determining the level of noise in data
Relief-F: estimating the influence of variables
depend on finding the k nearest neighbors of a point in a data frame or
matrix efficiently. (For large data sets it is not feasible to compute
the 'dist' matrix anyway.)
Seeing the proposed solution to "[R] distance between two matrices"
last month for finding _one_ nearest neighbor I came up with a solution
'nearest(A, n, k)' as appended.
Still, this is very clumsy and slow if you have to find the 3 nearest
neighbors for 1000 points in a data frame with 100000 entries at least
-- about 2 secs per data point on my computer or half an hour for an
application from real life.
Are there better/faster ways to perform this task using R functions?
Even better, is there a free implementation of kd-trees that I could
utilize (the one I found did not conform to the ANSI C standard)?
Someome pointed to 'spdep' of the R-Sig-Geo project, but 'knearneigh'
only accepts 2D data points.
Hans Werner Borchers
ABB Corporate Research, Germany
________________________________________________________________________
require (class)
nearest <- function (X, n, k=3)
## Find k nearest neighbors of X[n, ] in the data frame
## or matrix X, utilizing function knn1 k-times.
{
N <- nrow(X)
# inds contains the indices of nearest neighbors
inds <- c(n); i <- 0
while (i < k) {
# use knn1 for one index...
j <- as.integer(knn1(X [-inds, ], X[n, ], 1:(N-length(inds))))
# ...and change to true index of neighbor
inds <- c(inds, setdiff(1:N, inds)[j])
i <- i+1
}
# return nearest neighbor indices (without n, of course)
return(inds[-1])
}
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