AgglomerativeClustering
Agglomerative Clustering.
Recursively merges pair of clusters of sample data; uses linkage distance.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new AgglomerativeClustering(opts?: object): AgglomerativeClustering;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.affinity? | string | 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 linkage is “ward”, only “euclidean” is accepted. If “precomputed”, a distance matrix (instead of a similarity matrix) is needed as input for the fit method. Default Value 'euclidean' |
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. Default Value false |
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. Default Value 'auto' |
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. |
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. Default Value 'ward' |
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 set to undefined then “euclidean” is used. 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. Default Value 2 |
Returns
Defined in: generated/cluster/AgglomerativeClustering.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/cluster/AgglomerativeClustering.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/cluster/AgglomerativeClustering.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/cluster/AgglomerativeClustering.ts:21 (opens in a new tab)
id
string
Defined in: generated/cluster/AgglomerativeClustering.ts:18 (opens in a new tab)
opts
any
Defined in: generated/cluster/AgglomerativeClustering.ts:19 (opens in a new tab)
Accessors
children_
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.
Signature
children_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/cluster/AgglomerativeClustering.ts:414 (opens in a new tab)
distances_
Distances between nodes in the corresponding place in children\_. Only computed if distance\_threshold is used or compute\_distances is set to true.
Signature
distances_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/cluster/AgglomerativeClustering.ts:441 (opens in a new tab)
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/AgglomerativeClustering.ts:387 (opens in a new tab)
labels_
Cluster labels for each point.
Signature
labels_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/cluster/AgglomerativeClustering.ts:279 (opens in a new tab)
n_clusters_
The number of clusters found by the algorithm. If distance\_threshold=None, it will be equal to the given n\_clusters.
Signature
n_clusters_(): Promise<number>;Returns
Promise<number>
Defined in: generated/cluster/AgglomerativeClustering.ts:252 (opens in a new tab)
n_connected_components_
The estimated number of connected components in the graph.
Signature
n_connected_components_(): Promise<number>;Returns
Promise<number>
Defined in: generated/cluster/AgglomerativeClustering.ts:333 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/cluster/AgglomerativeClustering.ts:360 (opens in a new tab)
n_leaves_
Number of leaves in the hierarchical tree.
Signature
n_leaves_(): Promise<number>;Returns
Promise<number>
Defined in: generated/cluster/AgglomerativeClustering.ts:306 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/cluster/AgglomerativeClustering.ts:85 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/cluster/AgglomerativeClustering.ts:89 (opens in a new tab)
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/AgglomerativeClustering.ts:150 (opens in a new tab)
fit()
Fit the hierarchical clustering from features, or distance matrix.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | 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:167 (opens in a new tab)
fit_predict()
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.
Signature
fit_predict(opts: object): Promise<ArrayLike>;Parameters
| Name | 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. |
Returns
Promise<ArrayLike>
Defined in: generated/cluster/AgglomerativeClustering.ts:209 (opens in a new tab)
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/AgglomerativeClustering.ts:98 (opens in a new tab)