Reference

Top-level package for Shared Nearest Neighbors.

class shared_nearest_neighbors.SNN(n_neighbors=7, eps=5, min_samples=5, algorithm='auto', leaf_size=30, metric='euclidean', p=None, metric_params=None, dissimilarity_func=<function snn_dissimilarity_func>, n_jobs=None)

fit(X, y=None, sample_weight=None)

Perform SNN clustering from features or distance matrix

First calls NearestNeighbors to construct the neighborhood graph considering the params n_neighbors, n_jobs, algorithm, leaf_size, metric, p, metric_params

Parameters:

• X ({array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples)) – 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.
• y (Ignored) – Not used, present here for API consistency by convention.
• sample_weight (array-like of shape (n_samples,), default=None) – 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.

Returns:

self – Returns a fitted instance of self.

Return type:

object

neighborhood_dissimilarity_matrix(X) → scipy.sparse.csr.csr_matrix

Neighborhood similarity matrix

Computes the sparse neighborhood similarity matrix

Parameters:X ({array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples)) – 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. Returns:Sparse matrix of shape (n_samples, n_samples) Return type:csr_matrix