sNNclust {dbscan}

Shared Nearest Neighbor Clustering

Description

Implements the shared nearest neighbor clustering algorithm by Ertoz, Steinbach and Kumar (2003).

Usage

sNNclust(x, k, eps, minPts, borderPoints = TRUE, ...)

Arguments

x

a data matrix/data.frame (Euclidean distance is used), a precomputed dist object or a kNN object created with kNN().

k

Neighborhood size for nearest neighbor sparsification to create the shared NN graph.

eps

Two objects are only reachable from each other if they share at least eps nearest neighbors. Note: this is different from the eps in DBSCAN!

minPts

minimum number of points that share at least eps nearest neighbors for a point to be considered a core points.

borderPoints

should border points 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.

Details

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 equivalent to the DBSCAN algorithm (see dbscan()) and that eps has a different meaning than for DBSCAN. Here it is a threshold on the number of shared neighbors (see sNN()) which defines a similarity.

Value

A object of class general_clustering with the following components:

cluster

A integer vector with cluster assignments. Zero indicates noise points.

type

name of used clustering algorithm.

param

list of used clustering parameters.

Author(s)

Michael Hahsler

References

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. doi:10.1137/1.9781611972733.5

See Also

Other clustering functions: dbscan(), extractFOSC(), hdbscan(), jpclust(), optics()

Examples

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)
cl

plot(DS3, col = cl$cluster + 1L, cex = .5)