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Implements the shared nearest neighbor clustering algorithm by Ertoz, Steinbach and Kumar.
sNNclust(x, k, eps, minPts, borderPoints = TRUE, ...)a data matrix/data.frame (Euclidean distance is used), a precomputed
dist object or a kNN object created with kNN().
Neighborhood size for nearest neighbor sparsification to create the shared NN graph.
Two objects are only reachable from each other if they share
at least eps nearest neighbors.
minimum number of points that share at least eps
nearest neighbors for a point to be considered a core points.
should borderPoints be assigned to clusters like in DBSCAN?
additional arguments are passed on to the k
nearest neighbor search algorithm. See kNN for
details on how to control the search strategy.
A object of class 'general_clustering' with the following components:
A integer vector with cluster assignments. Zero indicates noise points.
name of used clustering algorithm.
list of used clustering parameters.
Algorithm:
1) Constructs a shared nearest neighbor graph for a given k. The edge weights are the number of shared k nearest neighbors (in the range of [0, k]).
2) Find each points SNN density, i.e., the number of points which have a similarity of eps or greater.
3) Find the core points, i.e., all points that have an SNN density greater than MinPts.
4) Form clusters from the core points and assign border points (i.e., non-core points which share at least eps neighbors with a core point).
Note that steps 2-4 are DBSCAN and that eps is used on a similarity (the number of shared neighbors) and not on a distance like in DBSCAN.
Levent Ertoz, Michael Steinbach, Vipin Kumar, Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data, SIAM International Conference on Data Mining, 2003, 47-59.
# NOT RUN {
data("DS3")
# Out of k = 20 NN 7 (eps) have to be shared to create a link in the sNN graph.
# A point needs a least 16 (minPts) links in the sNN graph to be a core point.
# Noise points have cluster id 0 and are shown in black.
cl <- sNNclust(DS3, k = 20, eps = 7, minPts = 16)
plot(DS3, col = cl$cluster + 1L, cex = .5)
# }
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