# frNN: Find the Fixed Radius Nearest Neighbors In dbscan: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms

 frNN R Documentation

## Find the Fixed Radius Nearest Neighbors

### Description

This function uses a kd-tree to find the fixed radius nearest neighbors (including distances) fast.

### Usage

frNN(
x,
eps,
query = NULL,
sort = TRUE,
search = "kdtree",
bucketSize = 10,
splitRule = "suggest",
approx = 0
)

## S3 method for class 'frNN'
sort(x, decreasing = FALSE, ...)

## S3 method for class 'frNN'

## S3 method for class 'frNN'
print(x, ...)

### Arguments

 x a data matrix, a dist object or a frNN object. eps neighbors radius. query a data matrix with the points to query. If query is not specified, the NN for all the points in x is returned. If query is specified then x needs to be a data matrix. sort sort the neighbors by distance? This is expensive and can be done later using sort(). search nearest neighbor search strategy (one of "kdtree", "linear" or "dist"). bucketSize max size of the kd-tree leafs. splitRule 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. approx 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. decreasing sort in decreasing order? ... further arguments

### Details

If x is specified as a data matrix, then Euclidean distances an fast nearest neighbor lookup using a kd-tree are used.

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).

Self-matches: Self-matches are not returned!

### Value

frNN() returns an object of class frNN (subclass of NN) containing a list with the following components:

 id a list of integer vectors. Each vector contains the ids of the fixed radius nearest neighbors. dist a list with distances (same structure as id). eps neighborhood radius eps that was used.

adjacencylist() returns a list with one entry per data point in x. Each entry contains the id of the nearest neighbors.

Michael Hahsler

### References

David M. Mount and Sunil Arya (2010). ANN: A Library for Approximate Nearest Neighbor Searching, http://www.cs.umd.edu/~mount/ANN/.

Other NN functions: NN, comps(), kNNdist(), kNN(), sNN()

### Examples

data(iris)
x <- iris[, -5]

# Example 1: Find fixed radius nearest neighbors for each point
nn <- frNN(x, eps = .5)

# Number of neighbors
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"))

# plot the fixed radius neighbors (and then reduced to a radius of .3)
plot(nn, x)
plot(frNN(nn, eps = .3), x)

## Example 2: find fixed-radius NN for query points
q <- x[c(1,100),]
nn <- frNN(x, eps = .5, query = q)

plot(nn, x, col = "grey")
points(q, pch = 3, lwd = 2)

dbscan documentation built on Oct. 29, 2022, 1:13 a.m.