Note
This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.
nilearn.regions.recursive_neighbor_agglomeration#
- nilearn.regions.recursive_neighbor_agglomeration(X, mask_img, n_clusters, n_iter=10, threshold=1e-07, verbose=0)[source]#
Recursive neighbor agglomeration (ReNA): it performs iteratively the nearest neighbor grouping. See 1.
- Parameters
- X
numpy.ndarray Training data. shape = [n_samples, n_features]
- mask_imgNiimg-like object
Object used for masking the data.
- n_clusters
int The number of clusters to find.
- n_iter
int, optional Number of iterations. Default=10.
- threshold
floatin the close interval [0, 1], optional The threshold is set to handle eccentricities. Default=1e-7.
- verbose
int, optional Verbosity level. Default=0.
- X
- Returns
- n_components
int Number of clusters.
- labels
numpy.ndarray Cluster assignation. shape = [n_features]
- n_components
References
- 1
Andres Hoyos-Idrobo, Gael Varoquaux, Jonas Kahn, and Bertrand Thirion. Recursive nearest agglomeration (rena): fast clustering for approximation of structured signals. IEEE Trans. Pattern Anal. Mach. Intell., 41(3):669–681, 3 2019. URL: https://doi.org/10.1109/TPAMI.2018.2815524, doi:10.1109/TPAMI.2018.2815524.