Class: HDBSCAN

Cluster data using hierarchical density-based clustering.

HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. Read more in the User Guide.

For an example of how to use HDBSCAN, as well as a comparison to DBSCAN, please see the plotting demo.

Python Reference

Constructors

new HDBSCAN()

new HDBSCAN(opts?): HDBSCAN

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.algorithm`?	`"auto"` \| `"ball_tree"` \| `"kd_tree"` \| `"brute"`	Exactly which algorithm to use for computing core distances; By default this is set to `"auto"` which attempts to use a `KDTree` tree if possible, otherwise it uses a `BallTree` tree. Both `"kd_tree"` and `"ball_tree"` algorithms use the `NearestNeighbors` estimator. If the `X` passed during `fit` is sparse or `metric` is invalid for both `KDTree` and `BallTree`, then it resolves to use the `"brute"` algorithm.
`opts.allow_single_cluster`?	`boolean`	By default HDBSCAN* will not produce a single cluster, setting this to `true` will override this and allow single cluster results in the case that you feel this is a valid result for your dataset.
`opts.alpha`?	`number`	A distance scaling parameter as used in robust single linkage. See [3] for more information.
`opts.cluster_selection_epsilon`?	`number`	A distance threshold. Clusters below this value will be merged. See [5] for more information.
`opts.cluster_selection_method`?	`"eom"` \| `"leaf"`	The method used to select clusters from the condensed tree. The standard approach for HDBSCAN* is to use an Excess of Mass (`"eom"`) algorithm to find the most persistent clusters. Alternatively you can instead select the clusters at the leaves of the tree – this provides the most fine grained and homogeneous clusters.
`opts.copy`?	`boolean`	If `copy=True` then any time an in-place modifications would be made that would overwrite data passed to fit, a copy will first be made, guaranteeing that the original data will be unchanged. Currently, it only applies when `metric="precomputed"`, when passing a dense array or a CSR sparse matrix and when `algorithm="brute"`.
`opts.leaf_size`?	`number`	Leaf size for trees responsible for fast nearest neighbour queries when a KDTree or a BallTree are used as core-distance algorithms. A large dataset size and small `leaf_size` may induce excessive memory usage. If you are running out of memory consider increasing the `leaf_size` parameter. Ignored for `algorithm="brute"`.
`opts.max_cluster_size`?	`number`	A limit to the size of clusters returned by the `"eom"` cluster selection algorithm. There is no limit when `max_cluster_size=None`. Has no effect if `cluster_selection_method="leaf"`.
`opts.metric`?	`string`	The metric to use when calculating distance between instances in a feature array.
`opts.metric_params`?	`any`	Arguments passed to the distance metric.
`opts.min_cluster_size`?	`number`	The minimum number of samples in a group for that group to be considered a cluster; groupings smaller than this size will be left as noise.
`opts.min_samples`?	`number`	The parameter `k` used to calculate the distance between a point `x_p` and its k-th nearest neighbor. When `undefined`, defaults to `min_cluster_size`.
`opts.n_jobs`?	`number`	Number of jobs to run in parallel to calculate distances. `undefined` means 1 unless in a `joblib.parallel_backend` context. `\-1` means using all processors. See Glossary for more details.
`opts.store_centers`?	`string`	Which, if any, cluster centers to compute and store. The options are:

Returns HDBSCAN

Defined in generated/cluster/HDBSCAN.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/cluster/HDBSCAN.ts:23
`_isInitialized`	`boolean`	`false`	generated/cluster/HDBSCAN.ts:22
`_py`	`PythonBridge`	`undefined`	generated/cluster/HDBSCAN.ts:21
`id`	`string`	`undefined`	generated/cluster/HDBSCAN.ts:18
`opts`	`any`	`undefined`	generated/cluster/HDBSCAN.ts:19

Accessors

centroids_

Get Signature

get centroids_(): Promise<ArrayLike[]>

A collection containing the centroid of each cluster calculated under the standard euclidean metric. The centroids may fall “outside” their respective clusters if the clusters themselves are non-convex.

Note that n_clusters only counts non-outlier clusters. That is to say, the \-1, \-2, \-3 labels for the outlier clusters are excluded.

Returns Promise<ArrayLike[]>

Defined in generated/cluster/HDBSCAN.ts:441

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

Names of features seen during fit. Defined only when X has feature names that are all strings.

Returns Promise<ArrayLike>

Defined in generated/cluster/HDBSCAN.ts:414

labels_

Get Signature

get labels_(): Promise<ArrayLike>

Cluster labels for each point in the dataset given to fit. Outliers are labeled as follows:

Returns Promise<ArrayLike>

Defined in generated/cluster/HDBSCAN.ts:341

medoids_

Get Signature

get medoids_(): Promise<ArrayLike[]>

A collection containing the medoid of each cluster calculated under the whichever metric was passed to the metric parameter. The medoids are points in the original cluster which minimize the average distance to all other points in that cluster under the chosen metric. These can be thought of as the result of projecting the metric-based centroid back onto the cluster.

Note that n_clusters only counts non-outlier clusters. That is to say, the \-1, \-2, \-3 labels for the outlier clusters are excluded.

Returns Promise<ArrayLike[]>

Defined in generated/cluster/HDBSCAN.ts:466

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/cluster/HDBSCAN.ts:389

probabilities_

Get Signature

get probabilities_(): Promise<ArrayLike>

The strength with which each sample is a member of its assigned cluster.

Returns Promise<ArrayLike>

Defined in generated/cluster/HDBSCAN.ts:364

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/cluster/HDBSCAN.ts:120

Methods

dbscan_clustering()

dbscan_clustering(opts): Promise<ArrayLike>

Return clustering given by DBSCAN without border points.

Return clustering that would be equivalent to running DBSCAN* for a particular cut_distance (or epsilon) DBSCAN* can be thought of as DBSCAN without the border points. As such these results may differ slightly from cluster.DBSCAN due to the difference in implementation over the non-core points.

This can also be thought of as a flat clustering derived from constant height cut through the single linkage tree.

This represents the result of selecting a cut value for robust single linkage clustering. The min_cluster_size allows the flat clustering to declare noise points (and cluster smaller than min_cluster_size).

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.cut_distance`?	`number`	The mutual reachability distance cut value to use to generate a flat clustering.
`opts.min_cluster_size`?	`number`	Clusters smaller than this value with be called ‘noise’ and remain unclustered in the resulting flat clustering.