Class: SpectralClustering

Apply clustering to a projection of the normalized Laplacian.

In practice Spectral Clustering is very useful when the structure of the individual clusters is highly non-convex, or more generally when a measure of the center and spread of the cluster is not a suitable description of the complete cluster, such as when clusters are nested circles on the 2D plane.

If the affinity matrix is the adjacency matrix of a graph, this method can be used to find normalized graph cuts [1], [2].

When calling fit, an affinity matrix is constructed using either a kernel function such the Gaussian (aka RBF) kernel with Euclidean distance d(X, X):

Python Reference

Constructors

new SpectralClustering()

new SpectralClustering(opts?): SpectralClustering

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.affinity`?	`string`	‘nearest_neighbors’: construct the affinity matrix by computing a graph of nearest neighbors.
`opts.assign_labels`?	`"kmeans"` \| `"discretize"` \| `"cluster_qr"`	The strategy for assigning labels in the embedding space. There are two ways to assign labels after the Laplacian embedding. k-means is a popular choice, but it can be sensitive to initialization. Discretization is another approach which is less sensitive to random initialization [3]. The cluster_qr method [5] directly extract clusters from eigenvectors in spectral clustering. In contrast to k-means and discretization, cluster_qr has no tuning parameters and runs no iterations, yet may outperform k-means and discretization in terms of both quality and speed.
`opts.coef0`?	`number`	Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.
`opts.degree`?	`number`	Degree of the polynomial kernel. Ignored by other kernels.
`opts.eigen_solver`?	`"arpack"` \| `"lobpcg"` \| `"amg"`	The eigenvalue decomposition strategy to use. AMG requires pyamg to be installed. It can be faster on very large, sparse problems, but may also lead to instabilities. If `undefined`, then `'arpack'` is used. See [4] for more details regarding `'lobpcg'`.
`opts.eigen_tol`?	`number`	Stopping criterion for eigen decomposition of the Laplacian matrix. If `eigen_tol="auto"` then the passed tolerance will depend on the `eigen_solver`:
`opts.gamma`?	`number`	Kernel coefficient for rbf, poly, sigmoid, laplacian and chi2 kernels. Ignored for `affinity='nearest_neighbors'`, `affinity='precomputed'` or `affinity='precomputed_nearest_neighbors'`.
`opts.kernel_params`?	`any`	Parameters (keyword arguments) and values for kernel passed as callable object. Ignored by other kernels.
`opts.n_clusters`?	`number`	The dimension of the projection subspace.
`opts.n_components`?	`number`	Number of eigenvectors to use for the spectral embedding. If `undefined`, defaults to `n_clusters`.
`opts.n_init`?	`number`	Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. Only used if `assign_labels='kmeans'`.
`opts.n_jobs`?	`number`	The number of parallel jobs to run when `affinity='nearest_neighbors'` or `affinity='precomputed_nearest_neighbors'`. The neighbors search will be done in parallel. `undefined` means 1 unless in a `joblib.parallel_backend` context. `\-1` means using all processors. See Glossary for more details.
`opts.n_neighbors`?	`number`	Number of neighbors to use when constructing the affinity matrix using the nearest neighbors method. Ignored for `affinity='rbf'`.
`opts.random_state`?	`number`	A pseudo random number generator used for the initialization of the lobpcg eigenvectors decomposition when `eigen_solver \== 'amg'`, and for the K-Means initialization. Use an int to make the results deterministic across calls (See Glossary).
`opts.verbose`?	`boolean`	Verbosity mode.

Returns SpectralClustering

Defined in generated/cluster/SpectralClustering.ts:27

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/cluster/SpectralClustering.ts:25
`_isInitialized`	`boolean`	`false`	generated/cluster/SpectralClustering.ts:24
`_py`	`PythonBridge`	`undefined`	generated/cluster/SpectralClustering.ts:23
`id`	`string`	`undefined`	generated/cluster/SpectralClustering.ts:20
`opts`	`any`	`undefined`	generated/cluster/SpectralClustering.ts:21

Accessors

affinity_matrix_

Get Signature

get affinity_matrix_(): Promise<ArrayLike[]>

Affinity matrix used for clustering. Available only after calling fit.

Returns Promise<ArrayLike[]>

Defined in generated/cluster/SpectralClustering.ts:318

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/SpectralClustering.ts:399

labels_

Get Signature

get labels_(): Promise<ArrayLike>

Labels of each point

Returns Promise<ArrayLike>

Defined in generated/cluster/SpectralClustering.ts:345

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/cluster/SpectralClustering.ts:372

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/SpectralClustering.ts:127

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/SpectralClustering.ts:183

fit()

fit(opts): Promise<any>

Perform spectral clustering from features, or affinity matrix.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	Training instances to cluster, similarities / affinities between instances if `affinity='precomputed'`, or distances between instances if `affinity='precomputed_nearest_neighbors`. If a sparse matrix is provided in a format other than `csr_matrix`, `csc_matrix`, or `coo_matrix`, it will be converted into a sparse `csr_matrix`.
`opts.y`?	`any`	Not used, present here for API consistency by convention.

Returns Promise<any>

Defined in generated/cluster/SpectralClustering.ts:200

fit_predict()

fit_predict(opts): Promise<ArrayLike>

Perform spectral clustering on X and return cluster labels.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	Training instances to cluster, similarities / affinities between instances if `affinity='precomputed'`, or distances between instances if `affinity='precomputed_nearest_neighbors`. If a sparse matrix is provided in a format other than `csr_matrix`, `csc_matrix`, or `coo_matrix`, it will be converted into a sparse `csr_matrix`.
`opts.y`?	`any`	Not used, present here for API consistency by convention.