- class sklearn.cluster.DBSCAN(eps=0.5, min_samples=5, metric='euclidean', algorithm='auto', leaf_size=30, p=None, random_state=None)¶
Perform DBSCAN clustering from vector array or distance matrix.
DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density.
eps : float, optional
The maximum distance between two samples for them to be considered as in the same neighborhood.
min_samples : int, optional
The number of samples in a neighborhood for a point to be considered as a core point.
metric : string, or callable
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 metrics.pairwise.calculate_distance for its metric parameter. If metric is “precomputed”, X is assumed to be a distance matrix and must be square.
random_state : numpy.RandomState, optional
The generator used to initialize the centers. Defaults to numpy.random.
See examples/plot_dbscan.py for an example.
Ester, M., H. P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996
core_sample_indices_ array, shape = [n_core_samples] Indices of core samples. components_ array, shape = [n_core_samples, n_features] Copy of each core sample found by training. labels_ array, shape = [n_samples] Cluster labels for each point in the dataset given to fit(). Noisy samples are given the label -1.
fit(X) Perform DBSCAN clustering from features or distance matrix. fit_predict(X[, y]) Performs clustering on X and returns cluster labels. get_params([deep]) Get parameters for this estimator. set_params(**params) Set the parameters of this estimator.
- __init__(eps=0.5, min_samples=5, metric='euclidean', algorithm='auto', leaf_size=30, p=None, random_state=None)¶
Perform DBSCAN clustering from features or distance matrix.
X: array [n_samples, n_samples] or [n_samples, n_features] :
Array of distances between samples, or a feature array. The array is treated as a feature array unless the metric is given as ‘precomputed’.
params: dict :
Overwrite keywords from __init__.
- fit_predict(X, y=None)¶
Performs clustering on X and returns cluster labels.
X : ndarray, shape (n_samples, n_features)
y : ndarray, shape (n_samples,)
Get parameters for this estimator.
deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
params : mapping of string to any
Parameter names mapped to their values.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :