dbscan (version 1.1-8)

dbscan: DBSCAN

Description

Fast reimplementation of the DBSCAN (Density-based spatial clustering of applications with noise) clustering algorithm using a kd-tree. The implementation is significantly faster and can work with larger data sets then dbscan in fpc.

Usage

dbscan(x, eps, minPts = 5, weights = NULL, borderPoints = TRUE, ...)
# S3 method for dbscan_fast
predict(object, newdata = NULL, data, ...)

Arguments

x

a data matrix, a data.frame, a dist object or a frNN object with fixed-radius nearest neighbors.

eps

size (radius) of the epsilon neighborhood. Can be omitted if x is a frNN object.

minPts

number of minimum points required in the eps neighborhood for core points (including the point itself). The default value is 5 points.

weights

numeric; weights for the data points. Only needed to perform weighted clustering.

borderPoints

logical; should border points be assigned to clusters. The default is TRUE for regular DBSCAN. If FALSE then border points are considered noise (see DBSCAN* in Campello et al, 2013).

...

additional arguments are passed on to the fixed-radius nearest neighbor search algorithm. See frNN for details on how to control the search strategy.

object

a DBSCAN clustering object for prediction.

data

the data set used to create the DBSCAN clustering object.

newdata

new data points for which the cluster membership should be predicted.

Value

An object of class 'dbscan_fast' with the following components:

eps

value of the eps parameter.

minPts

value of the minPts parameter.

cluster

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

Details

Note: use dbscan::dbscan to call this implementation when you also use package fpc.

The algorithm

This implementation of DBSCAN (Hahsler et al, 2019) implements the original algorithm as described by Ester et al (1996). DBSCAN estimates the density around each data point by counting the number of points in a user-specified eps-neighborhood and applies a used-specified minPts thresholds to identify core, border and noise points. In a second step, core points are joined into a cluster if they are density-reachable (i.e., there is a chain of core points where one falls inside the eps-neighborhood of the next). Finally, border points are assigned to clusters. The algorithm needs parameters eps (the radius of the epsilon neighborhood) and minPts (the density threshold).

Border points are arbitrarily assigned to clusters in the original algorithm. DBSCAN* (see Campello et al 2013) treats all border points as noise points. This is implemented with borderPoints = FALSE.

Specifying the data

If x is a matrix or a data.frame, then fast fixed-radius nearest neighbor computation using a kd-tree is performed using Euclidean distance. See frNN for more information on the parameters related to nearest neighbor search.

Any precomputed distance matrix (dist object) can be specified as x. You may run into memory issues since distance matrices are large.

A precomputed frNN object can be supplied as x. In this case eps does not need to be specified. This option us useful for large data sets, where a sparse distance matrix is available. See frNN how to create frNN objects.

Setting parameters for DBSCAN

The parameters minPts and eps depend on each other and changing one typically requires changing the other one as well. The original DBSCAN paper suggests to start by setting minPts to the dimensionality of the data plus one or higher. minPts defines the minimum density around a core point (i.e., the minimum density for non-noise areas). Increase the parameter to suppress more noise in the data and require more points to form a cluster. A suitable neighborhood size parameter eps given a fixed value for minPts can be found visually by inspecting the kNNdistplot of the data using k = minPts - 1 (minPts includes the point itself, while the k-nearest neighbors distance does not). The k-nearest neighbor distance plot sorts all data points by their k-nearest neighbor distance. A sudden increase of the kNN distance (a knee) indicates that the points to the right are most likely outliers. Choose eps for DBSCAN where the knee is.

Predict cluster memberships

predict can be used to predict cluster memberships for new data points. A point is considered a member of a cluster if it is within the eps neighborhood of a member of the cluster (Euclidean distance is used). Points which cannot be assigned to a cluster will be reported as members of the noise cluster 0.

References

Hahsler M, Piekenbrock M, Doran D (2019). dbscan: Fast Density-Based Clustering with R. Journal of Statistical Software, 91(1), 1-30. 10.18637/jss.v091.i01

Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Institute for Computer Science, University of Munich. Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), 226-231. https://dl.acm.org/doi/10.5555/3001460.3001507

Campello, R. J. G. B.; Moulavi, D.; Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery in Databases, PAKDD 2013, Lecture Notes in Computer Science 7819, p. 160. 10.1007/978-3-642-37456-2_14

See Also

kNNdistplot, frNN, dbscan in fpc.

Examples

# NOT RUN {
## Example 1: use dbscan on the iris data set
data(iris)
iris <- as.matrix(iris[,1:4])

## Find suitable DBSCAN parameters:
## 1. We use minPts = dim + 1 = 5 for iris. A larger value can also be used.
## 2. We inspect the  k-NN distance plot for k = minPts - 1 = 4
kNNdistplot(iris, k = 5 - 1)

## Noise seems to start around a 4-NN distance of .7
abline(h=.7, col = "red", lty=2)

## Cluster with the chosen parameters
res <- dbscan(iris, eps = .7, minPts = 5)
res

pairs(iris, col = res$cluster + 1L)

## Use a precomputed frNN object
fr <- frNN(iris, eps = .7)
dbscan(fr, minPts = 5)

## Example 2: use data from fpc
set.seed(665544)
n <- 100
x <- cbind(
  x = runif(10, 0, 10) + rnorm(n, sd = 0.2),
  y = runif(10, 0, 10) + rnorm(n, sd = 0.2)
  )

res <- dbscan(x, eps = .3, minPts = 3)
res

## plot clusters and add noise (cluster 0) as crosses.
plot(x, col=res$cluster)
points(x[res$cluster==0,], pch = 3, col = "grey")

hullplot(x, res)

## Predict cluster membership for new data points
## (Note: 0 means it is predicted as noise)
newdata <- x[1:5,] + rnorm(10, 0, .3)
hullplot(x, res)
points(newdata, pch = 3 , col = "red", lwd = 3)
text(newdata, pos = 1)

predict(res, newdata, data = x)

## Compare speed against fpc version (if microbenchmark is installed)
## Note: we use dbscan::dbscan to make sure that we do now run the
## implementation in fpc.
# }
# NOT RUN {
if (requireNamespace("fpc", quietly = TRUE) &&
    requireNamespace("microbenchmark", quietly = TRUE)) {
  t_dbscan <- microbenchmark::microbenchmark(
    dbscan::dbscan(x, .3, 3), times = 10, unit = "ms")
  t_dbscan_linear <- microbenchmark::microbenchmark(
    dbscan::dbscan(x, .3, 3, search = "linear"), times = 10, unit = "ms")
  t_dbscan_dist <- microbenchmark::microbenchmark(
    dbscan::dbscan(x, .3, 3, search = "dist"), times = 10, unit = "ms")
  t_fpc <- microbenchmark::microbenchmark(
    fpc::dbscan(x, .3, 3), times = 10, unit = "ms")

  r <- rbind(t_fpc, t_dbscan_dist, t_dbscan_linear, t_dbscan)
  r

  boxplot(r,
    names = c('fpc', 'dbscan (dist)', 'dbscan (linear)', 'dbscan (kdtree)'),
    main = "Runtime comparison in ms")

  ## speedup of the kd-tree-based version compared to the fpc implementation
  median(t_fpc$time) / median(t_dbscan$time)
}
# }
# NOT RUN {
## Example 3: manually create a frNN object for dbscan (dbscan only needs ids and eps)
nn <- structure(list(ids = list(c(2,3), c(1,3), c(1,2,3), c(3,5), c(4,5)), eps = 1),
  class =  c("NN", "frNN"))
nn
dbscan(nn, minPts = 2)
# }