Crate hdbscan

Hierarchical Density-Based Spatial Clustering of Applications with Noise (“HDBSCAN”) clustering algorithm in Rust. Generic over floating point numeric types.

HDBSCAN is a powerful clustering algorithm that can be used to effectively find clusters in real world data. The main benefits of HDBSCAN are that:

1. It does not assume that all data points belong to a cluster, as many clustering algorithms do. I.e. a data set can contain “noise” points. This is important for modelling real world data, which is inherently noisy;
2. It allows clusters of varying densities, unlike the plain DBSCAN algorithm which uses a static density threshold. The winning clusters are those that persist the longest at all densities. This is also crucial for modelling real world data; and
3. It makes no assumptions about the number of clusters there have to be, unlike KMeans clustering. The algorithm will just select the clusters that are the most persistent at all densities.

This implementation owes a debt to the Python scikit-learn implementation of this algorithm, without which this algorithm would not have been possible. The “How HDBSCAN works” article below is invaluable in understanding this algorithm better.

Examples

use std::collections::HashSet;
use hdbscan::Hdbscan;

let data: Vec<Vec<f32>> = vec![
    vec![1.5, 2.2],
    vec![1.0, 1.1],
    vec![1.2, 1.4],
    vec![0.8, 1.0],
    vec![1.1, 1.0],
    vec![3.7, 4.0],
    vec![3.9, 3.9],
    vec![3.6, 4.1],
    vec![3.8, 3.9],
    vec![4.0, 4.1],
    vec![10.0, 10.0],
];
let clusterer = Hdbscan::default(&data);
let labels = clusterer.cluster().unwrap();
//First five points form one cluster
assert_eq!(1, labels[..5].iter().collect::<HashSet<_>>().len());
// Next five points are a second cluster
assert_eq!(1, labels[5..10].iter().collect::<HashSet<_>>().len());
// The final point is noise
assert_eq!(-1, labels[10]);

References

• Campello, R.J.G.B.; Moulavi, D.; Sander, J. Density-based clustering based on hierarchical density estimates.
• How HDBSCAN Works

Structs

• Hdbscan
  The HDBSCAN clustering algorithm in Rust. Generic over floating point numeric types.
• HdbscanHyperParams
  A wrapper around the various hyper parameters used in HDBSCAN clustering. Only use if you want to tune hyper parameters. Otherwise use Hdbscan::default() to instantiate the model with default hyper parameters.
• HyperParamBuilder
  Builder object to set custom hyper parameters.

Enums

• Center
  Possible methodologies for calculating the center of clusters
• DistanceMetric
  Possible distance metrics that can be used in the HDBSCAN algorithm when calculating the distances between data points.
• HdbscanError
  Possible errors that arise due to issues with HDBSCAN input data.
• NnAlgorithm
  The nearest neighbour algorithm options