interdim.cluster

Functions

cluster_data(data[, method, n_clusters])

Perform clustering on the input data.

Classes

AffinityPropagation(*[, damping, max_iter, ...])

Perform Affinity Propagation Clustering of data.

AgglomerativeClustering([n_clusters, ...])

Agglomerative Clustering.

Birch(*[, threshold, branching_factor, ...])

Implements the BIRCH clustering algorithm.

DBSCAN([eps, min_samples, metric, ...])

Perform DBSCAN clustering from vector array or distance matrix.

GaussianMixture([n_components, ...])

Gaussian Mixture.

KMeans([n_clusters, init, n_init, max_iter, ...])

K-Means clustering.

MeanShift(*[, bandwidth, seeds, ...])

Mean shift clustering using a flat kernel.

MiniBatchKMeans([n_clusters, init, ...])

Mini-Batch K-Means clustering.

OPTICS(*[, min_samples, max_eps, metric, p, ...])

Estimate clustering structure from vector array.

SpectralClustering([n_clusters, ...])

Apply clustering to a projection of the normalized Laplacian.
interdim.cluster.cluster_data(data: ndarray, method: Literal['kmeans', 'dbscan', 'hdbscan', 'agglomerative', 'birch', 'mini_batch_kmeans', 'spectral', 'affinity_propagation', 'mean_shift', 'optics', 'gaussian_mixture'] = 'dbscan', n_clusters: int | None = None, **kwargs) → ndarray[source]

Perform clustering on the input data.

Parameters:

  • data – Input data to cluster.

  • method – Clustering method to use.

  • n_clusters – Number of clusters (for methods that require it).

  • **kwargs – Additional arguments to pass to the clustering method.

Returns:

Cluster labels for each data point.

Raises:

  • ValueError – If n_clusters is not provided for methods that require it.

  • ImportError – If HDBSCAN is not installed when ‘hdbscan’ method is selected.