Class: AgglomerativeClustering

Agglomerative Clustering.

Recursively merges pair of clusters of sample data; uses linkage distance.

Constructors

new AgglomerativeClustering()

new AgglomerativeClustering(opts?): AgglomerativeClustering

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.compute_distances`?	`boolean`	Computes distances between clusters even if `distance_threshold` is not used. This can be used to make dendrogram visualization, but introduces a computational and memory overhead.
`opts.compute_full_tree`?	`boolean` \| `"auto"`	Stop early the construction of the tree at `n_clusters`. This is useful to decrease computation time if the number of clusters is not small compared to the number of samples. This option is useful only when specifying a connectivity matrix. Note also that when varying the number of clusters and using caching, it may be advantageous to compute the full tree. It must be `true` if `distance_threshold` is not `undefined`. By default `compute_full_tree` is “auto”, which is equivalent to `true` when `distance_threshold` is not `undefined` or that `n_clusters` is inferior to the maximum between 100 or `0.02 \* n_samples`. Otherwise, “auto” is equivalent to `false`.
`opts.connectivity`?	`ArrayLike`	Connectivity matrix. Defines for each sample the neighboring samples following a given structure of the data. This can be a connectivity matrix itself or a callable that transforms the data into a connectivity matrix, such as derived from `kneighbors_graph`. Default is `undefined`, i.e, the hierarchical clustering algorithm is unstructured. For an example of connectivity matrix using `kneighbors_graph`, see Agglomerative clustering with and without structure.
`opts.distance_threshold`?	`number`	The linkage distance threshold at or above which clusters will not be merged. If not `undefined`, `n_clusters` must be `undefined` and `compute_full_tree` must be `true`.
`opts.linkage`?	`"ward"` \| `"complete"` \| `"average"` \| `"single"`	Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion.
`opts.memory`?	`string`	Used to cache the output of the computation of the tree. By default, no caching is done. If a string is given, it is the path to the caching directory.
`opts.metric`?	`string`	Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”, “manhattan”, “cosine”, or “precomputed”. If linkage is “ward”, only “euclidean” is accepted. If “precomputed”, a distance matrix is needed as input for the fit method.
`opts.n_clusters`?	`number`	The number of clusters to find. It must be `undefined` if `distance_threshold` is not `undefined`.

Returns AgglomerativeClustering

Defined in generated/cluster/AgglomerativeClustering.ts:25

Properties

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

Accessors

children_

Get Signature

get children_(): Promise<ArrayLike[]>

The children of each non-leaf node. Values less than n_samples correspond to leaves of the tree which are the original samples. A node i greater than or equal to n_samples is a non-leaf node and has children children_\[i \- n_samples\]. Alternatively at the i-th iteration, children[i][0] and children[i][1] are merged to form node n_samples + i.

Returns Promise<ArrayLike[]>

Defined in generated/cluster/AgglomerativeClustering.ts:437

distances_

Get Signature

get distances_(): Promise<ArrayLike>

Distances between nodes in the corresponding place in children_. Only computed if distance_threshold is used or compute_distances is set to true.

Returns Promise<ArrayLike>

Defined in generated/cluster/AgglomerativeClustering.ts:464

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/AgglomerativeClustering.ts:410

labels_

Get Signature

get labels_(): Promise<ArrayLike>

Cluster labels for each point.

Returns Promise<ArrayLike>

Defined in generated/cluster/AgglomerativeClustering.ts:302

n_clusters_

Get Signature

get n_clusters_(): Promise<number>

The number of clusters found by the algorithm. If distance_threshold=None, it will be equal to the given n_clusters.

Returns Promise<number>

Defined in generated/cluster/AgglomerativeClustering.ts:275

n_connected_components_

Get Signature

get n_connected_components_(): Promise<number>

The estimated number of connected components in the graph.

Returns Promise<number>

Defined in generated/cluster/AgglomerativeClustering.ts:356

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/cluster/AgglomerativeClustering.ts:383

n_leaves_

Get Signature

get n_leaves_(): Promise<number>

Number of leaves in the hierarchical tree.

Returns Promise<number>

Defined in generated/cluster/AgglomerativeClustering.ts:329

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/AgglomerativeClustering.ts:82

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

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

Returns Promise<void>

Defined in generated/cluster/AgglomerativeClustering.ts:138

fit()

fit(opts): Promise<any>

Fit the hierarchical clustering from features, or distance matrix.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	Training instances to cluster, or distances between instances if `metric='precomputed'`.
`opts.y`?	`any`	Not used, present here for API consistency by convention.

Returns Promise<any>

Defined in generated/cluster/AgglomerativeClustering.ts:155

fit_predict()

fit_predict(opts): Promise<ArrayLike>

Fit and return the result of each sample’s clustering assignment.

In addition to fitting, this method also return the result of the clustering assignment for each sample in the training set.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	Training instances to cluster, or distances between instances if `affinity='precomputed'`.
`opts.y`?	`any`	Not used, present here for API consistency by convention.