Contains 8000 2-d points, with 6 "natural" looking shapes, all of which have
an sinusoid-like shape that intersects with each cluster.
The data set was originally used as a benchmark data set for the Chameleon clustering
algorithm (Karypis, Han and Kumar, 1999) to
illustrate the a data set containing arbitrarily shaped
spatial data surrounded by both noise and artifacts.

datasets

A fast reimplementation of several density-based algorithms of
the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based
spatial clustering of applications with noise) and HDBSCAN (hierarchical
DBSCAN), the ordering algorithm OPTICS (ordering points to identify the
clustering structure), shared nearest neighbor clustering, and the outlier
detection algorithms LOF (local outlier factor) and GLOSH (global-local
outlier score from hierarchies). The implementations use the kd-tree data
structure (from library ANN) for faster k-nearest neighbor search. An R
interface to fast kNN and fixed-radius NN search is also provided.
Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.

Michael Hahsler

dbscan

Density-Based Spatial Clustering of Applications with Noise
(DBSCAN) and Related Algorithms

Matthew Piekenbrock

Sunil Arya

David Mount

DS3 function

A data.frame with 8000 observations on the following 2 columns:<dl>
<dt>X</dt>
<dd>a numeric vector</dd><dt>Y</dt>
<dd>a numeric vector</dd>
</dl>

Format

DS3: Spatial data with arbitrary shapes — DS3

A data.frame with 8000 observations on the following 2 columns:<dl>
<dt>X</dt>
<dd>a numeric vector</dd>

<dt>Y</dt>
<dd>a numeric vector</dd>


</dl>

DS3: DS3: Spatial data with arbitrary shapes

Description

Arguments

Format

References

Examples