Class to perform KCenter clustering of a given data set
KCenter randomly picks one point from the data, which is now the center of the first cluster. All points are put into the first cluster. In general the furthest point from its center is chosen to be the new center. All points, which are closer to the new center than the old one are assigned to the new cluster. This goes on, until K clusters have been created.
n_clusters (int) – desired number of clusters
>>> cluster = KCenter(n_cluster=200) >>> cluster.fit(data)
Compute the centroids of data.
data (np.ndarray) – A 2D array of data. Columns are features and rows are data examples.
Perform clustering on X and returns cluster labels.
X (ndarray, shape (n_samples, n_features)) – Input data.
y (Ignored) – Not used, present for API consistency by convention.
labels – Cluster labels.
ndarray, shape (n_samples,)
fit_transform(X, y=None, **fit_params)¶
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
X (numpy array of shape [n_samples, n_features]) – Training set.
y (numpy array of shape [n_samples]) – Target values.
**fit_params (dict) – Additional fit parameters.
X_new – Transformed array.
numpy array of shape [n_samples, n_features_new]
Get parameters for this estimator.
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
params – Parameter names mapped to their values.
mapping of string to any
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter> so that it’s possible to update each
component of a nested object.