dbscan (version 1.1-8)

hdbscan: HDBSCAN

Description

Fast implementation of the HDBSCAN (Hierarchical DBSCAN) and its related algorithms using Rcpp.

Usage

hdbscan(x, minPts,
    gen_hdbscan_tree = FALSE,
    gen_simplified_tree = FALSE)
# S3 method for hdbscan
print(x, ...)
# S3 method for hdbscan
plot(x, scale="suggest",
    gradient=c("yellow", "red"), show_flat = FALSE, ...)

Arguments

x

a data matrix (Euclidean distances are used) or a dist object calculated with an arbitrary distance metric.

minPts

integer; Minimum size of clusters. See details.

gen_hdbscan_tree

logical; should the robust single linkage tree be explicitly computed. (see cluster tree in Chaudhuri et al, 2010).

gen_simplified_tree

logical; should the simplified hierarchy be explicitly computed. (see Campello et al, 2013).

...

additional arguments are passed on to the appropriate S3 methods (such as plotting parameters).

scale

integer; used to scale condensed tree based on the graphics device. Lower scale results in wider trees.

gradient

character vector; the colors to build the condensed tree coloring with.

show_flat

logical; whether to draw boxes indicating the most stable clusters.

Value

A object of class 'hdbscan' with the following components:

cluster

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

minPts

value of the minPts parameter.

cluster_scores

The sum of the stability scores for each salient ('flat') cluster. Corresponds to cluster ids given the in 'cluster' member.

membership_prob

The 'probability' or individual stability of a point within its clusters. Between 0 and 1.

outlier_scores

The outlier score (GLOSH) of each point.

hc

An 'hclust' object of the HDBSCAN hierarchy.

%% ...

Details

This fast implementation of HDBSCAN (Hahsler et al, 2019) computes the hierarchical cluster tree representing density estimates along with the stability-based flat cluster extraction proposed by Campello et al. (2013). HDBSCAN essentially computes the hierarchy of all DBSCAN* clusterings, and then uses a stability-based extraction method to find optimal cuts in the hierarchy, thus producing a flat solution.

Additional, related algorithms including the "Global-Local Outlier Score from Hierarchies" (GLOSH) (see section 6 of Campello et al., 2015) outlier scores and ability to cluster based on instance-level constraints (see section 5.3 of Campello et al. 2015) are supported. The algorithms only need the parameter minPts.

Note that minPts not only acts as a minimum cluster size to detect, but also as a "smoothing" factor of the density estimates implicitly computed from HDBSCAN.

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

Campello RJGB, 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

Campello RJGB, Moulavi D, Zimek A, Sander J (2015). Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 10(5):1-51. 10.1145/2733381

See Also

dbscan

Examples

# NOT RUN {
## cluster the moons data set with HDBSCAN
data(moons)

res <- hdbscan(moons, minPts = 5)
res

plot(res)
plot(moons, col = res$cluster + 1L)

## cluster the moons data set with HDBSCAN using Manhattan distances
res <- hdbscan(dist(moons, method = "manhattan"), minPts = 5)
plot(res)
plot(moons, col = res$cluster + 1L)

## DS3 from Chameleon
data("DS3")

res <- hdbscan(DS3, minPts = 50)
res

## Plot the simplified tree, highlight the most stable clusters
plot(res, show_flat = TRUE)

## Plot the actual clusters
plot(DS3, col=res$cluster+1L, cex = .5)
# }