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DBSCAN

Perform DBSCAN clustering from vector array or distance matrix.

DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density.

The worst case memory complexity of DBSCAN is \(O({n}^2)\), which can occur when the eps param is large and min\_samples is low.

Read more in the User Guide.

Python Reference

Constructors

constructor()

Signature

new DBSCAN(opts?: object): DBSCAN;

Parameters

Name Type Description
opts? object -
opts.algorithm? "auto" | "ball_tree" | "kd_tree" | "brute" The algorithm to be used by the NearestNeighbors module to compute pointwise distances and find nearest neighbors. See NearestNeighbors module documentation for details. Default Value 'auto'
opts.eps? number The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. Default Value 0.5
opts.leaf_size? number Leaf size passed to BallTree or cKDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. Default Value 30
opts.metric? any The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by sklearn.metrics.pairwise\_distances for its metric parameter. If metric is “precomputed”, X is assumed to be a distance matrix and must be square. X may be a sparse graph, in which case only “nonzero” elements may be considered neighbors for DBSCAN. Default Value 'euclidean'
opts.metric_params? any Additional keyword arguments for the metric function.
opts.min_samples? number The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself. If min\_samples is set to a higher value, DBSCAN will find denser clusters, whereas if it is set to a lower value, the found clusters will be more sparse. Default Value 5
opts.n_jobs? number The number of parallel jobs to run. undefined means 1 unless in a joblib.parallel\_backend context. \-1 means using all processors. See Glossary for more details.
opts.p? number The power of the Minkowski metric to be used to calculate distance between points. If undefined, then p=2 (equivalent to the Euclidean distance).

Returns

DBSCAN

Defined in: generated/cluster/DBSCAN.ts:27

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/cluster/DBSCAN.ts:142

fit()

Perform DBSCAN clustering from features, or distance matrix.

Signature

fit(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.X? any Training instances to cluster, or distances between instances if metric='precomputed'. If a sparse matrix is provided, it will be converted into a sparse csr\_matrix.
opts.sample_weight? ArrayLike Weight of each sample, such that a sample with a weight of at least min\_samples is by itself a core sample; a sample with a negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1.
opts.y? any Not used, present here for API consistency by convention.

Returns

Promise<any>

Defined in: generated/cluster/DBSCAN.ts:159

fit_predict()

Compute clusters from a data or distance matrix and predict labels.

Signature

fit_predict(opts: object): Promise<ArrayLike>;

Parameters

Name Type Description
opts object -
opts.X? any Training instances to cluster, or distances between instances if metric='precomputed'. If a sparse matrix is provided, it will be converted into a sparse csr\_matrix.
opts.sample_weight? ArrayLike Weight of each sample, such that a sample with a weight of at least min\_samples is by itself a core sample; a sample with a negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1.
opts.y? any Not used, present here for API consistency by convention.

Returns

Promise<ArrayLike>

Defined in: generated/cluster/DBSCAN.ts:206

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.routing? any A MetadataRequest encapsulating routing information.

Returns

Promise<any>

Defined in: generated/cluster/DBSCAN.ts:255

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name Type
py PythonBridge

Returns

Promise<void>

Defined in: generated/cluster/DBSCAN.ts:95

set_fit_request()

Request metadata passed to the fit method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

set_fit_request(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.sample_weight? string | boolean Metadata routing for sample\_weight parameter in fit.

Returns

Promise<any>

Defined in: generated/cluster/DBSCAN.ts:292

Properties

_isDisposed

boolean = false

Defined in: generated/cluster/DBSCAN.ts:25

_isInitialized

boolean = false

Defined in: generated/cluster/DBSCAN.ts:24

_py

PythonBridge

Defined in: generated/cluster/DBSCAN.ts:23

id

string

Defined in: generated/cluster/DBSCAN.ts:20

opts

any

Defined in: generated/cluster/DBSCAN.ts:21

Accessors

components_

Copy of each core sample found by training.

Signature

components_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/cluster/DBSCAN.ts:350

core_sample_indices_

Indices of core samples.

Signature

core_sample_indices_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/cluster/DBSCAN.ts:325

feature_names_in_

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

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/cluster/DBSCAN.ts:418

labels_

Cluster labels for each point in the dataset given to fit(). Noisy samples are given the label -1.

Signature

labels_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/cluster/DBSCAN.ts:373

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/cluster/DBSCAN.ts:395

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/cluster/DBSCAN.ts:82

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name Type
pythonBridge PythonBridge

Returns

void

Defined in: generated/cluster/DBSCAN.ts:86