| sNN {dbscan} | R Documentation |
Calculates the number of shared nearest neighbors, the shared nearest neighbor similarity and creates a shared nearest neighbors graph.
sNN(x, k, kt = NULL, jp = FALSE, sort = TRUE, search = "kdtree", bucketSize = 10, splitRule = "suggest", approx = 0)
x |
a data matrix, a dist object or a kNN object. |
k |
number of neighbors to consider to calculate the shared nearest neighbors. |
kt |
minimum threshold on the number of shared nearest neighbors to build the shared
nearest neighbor graph. Edges
are only preserved if |
jp |
use the definition by Javis and Patrick (1973), where shared neighbors are only counted between points that are in each other's neighborhood, otherwise 0 is returned. If FALSE, then the number of shared neighbors is returned, even if the points are not neighbors. |
search |
nearest neighbor search strategy (one of "kdtree", "linear" or "dist"). |
sort |
sort by the number of shared nearest neighbors? Note that this is expensive and
|
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+ |
The number of shared nearest neighbors is the intersection of the kNN neighborhood of two points. Note: that each point is considered to be part of its own kNN neighborhood. The range for the shared nearest neighbors is [0,k].
Javis and Patrick (1973) use the shared nearest neighbor graph for clustering. They only count shared neighbors between points that are in each other's kNN neighborhood.
An object of class sNN (subclass of kNN and NN) containing a list with the following components:
id |
a matrix with ids. |
dist |
a matrix with the distances. |
shared |
a matrix with the number of shared nearest neighbors. |
k |
number of k used. |
Michael Hahsler
R. A. Jarvis and E. A. Patrick. 1973. Clustering Using a Similarity Measure Based on Shared Near Neighbors. IEEE Trans. Comput. 22, 11 (November 1973), 1025-1034. doi: 10.1109/T-C.1973.223640
NN and kNN for k nearest neighbors.
data(iris) x <- iris[, -5] # finding kNN and add the number of shared nearest neighbors. k <- 5 nn <- sNN(x, k = k) nn # shared nearest neighbor distribution table(as.vector(nn$shared)) # explore neighborhood of point 10 i <- 10 nn$shared[i,] plot(nn, x) # apply a threshold to create a sNN graph with edges # if more than 3 neighbors are shared. nn_3 <- sNN(nn, kt = 3) plot(nn_3, x) # get an adjacency list for the shared nearest neighbor graph adjacencylist(nn_3)