moons: Moons Data

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

Moons Data

Description

Contains 100 2-d points, half of which are contained in two moons or "blobs"" (25 points each blob), and the other half in asymmetric facing crescent shapes. The three shapes are all linearly separable.

Format

A data frame with 100 observations on the following 2 variables.

X

a numeric vector

Y

a numeric vector

Details

This data was generated with the following Python commands using the SciKit-Learn library:

⁠> import sklearn.datasets as data⁠

⁠> moons = data.make_moons(n_samples=50, noise=0.05)⁠

⁠> blobs = data.make_blobs(n_samples=50, centers=[(-0.75,2.25), (1.0, 2.0)], cluster_std=0.25)⁠

⁠> test_data = np.vstack([moons, blobs])⁠

Source

See the HDBSCAN notebook from github documentation: http://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html

References

Pedregosa, Fabian, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel et al. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, no. Oct (2011): 2825-2830.

Examples

data(moons)
plot(moons, pch=20)

dbscan documentation built on June 29, 2024, 1:07 a.m.