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This function uses a kd-tree to find the fixed radius nearest neighbors (including distances) fast.
frNN(x, eps, sort = TRUE, search = "kdtree", bucketSize = 10,
splitRule = "suggest", approx = 0)a data matrix, a dist object or a frNN object.
neighbors radius.
nearest neighbor search strategy (one of "kdtree" or "linear", "dist").
sort the neighbors by distance? This is expensive and can be done later using sort().
max size of the kd-tree leafs.
rule to split the kd-tree. One of "STD", "MIDPT", "FAIR", "SL_MIDPT", "SL_FAIR" or "SUGGEST" (SL stands for sliding). "SUGGEST" uses ANNs best guess.
use approximate nearest neighbors. All NN up to a distance of
a factor of 1+approx eps may be used. Some actual NN may be
omitted leading to spurious clusters and noise points.
However, the algorithm will enjoy a significant speedup.
An object of class frNN (subclass of NN) containing a list with the following components:
a list of integer vectors. Each vector contains the ids of the fixed radius nearest neighbors.
a list with distances (same structure as ids).
eps used.
For details on the parameters see kNN.
Note: self-matches are not returned!
To create a frNN object from scratch, you need to supply at least the
elements id with a list of integer vectors with
the nearest neighbor ids
for each point and eps (see below).
David M. Mount and Sunil Arya (2010). ANN: A Library for Approximate Nearest Neighbor Searching, http://www.cs.umd.edu/~mount/ANN/.
# NOT RUN {
data(iris)
x <- iris[, -5]
# Find fixed radius nearest neighbors for each point
nn <- frNN(x, eps=.5)
# Number of neighbors
hist(sapply(adjacencylist(nn), length),
xlab = "k", main="Number of Neighbors",
sub = paste("Neighborhood size eps =", nn$eps))
# Explore neighbors of point i = 10
i <- 10
nn$id[[i]]
nn$dist[[i]]
plot(x, col = ifelse(1:nrow(iris) %in% nn$id[[i]], "red", "black"))
# get an adjacency list
head(adjacencylist(nn))
# plot the fixed radius neighbors (and then reduced to a radius of .3)
plot(nn, x)
plot(frNN(nn, .3), x)
## manually create a frNN object for dbscan (dbscan only needs ids and eps)
nn <- list(ids = list(c(2,3), c(1,3), c(1,2,3), c(3,5), c(4,5)), eps = 1)
class(nn) <- c("NN", "frNN")
nn
dbscan(nn, minPts = 2)
# }
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