Clusterer

TimeSeriesKvisibility

Predict

Kvisibility for time series clustering.

Quickstart

python

from sktime.clustering.kvisibility import TimeSeriesKvisibility

estimator = TimeSeriesKvisibility(n_clusters=5, init='k-means++', n_init=4)

init{‘k-means++’, ‘random’}, callable or: array-like of shape (n_clusters, n_features), default=’k-means++’ Method for initialization: ‘k-means++’: selects initial cluster centroids using sampling based on an empirical probability distribution of the points’ contribution to the overall inertia. This technique speeds up convergence. The algorithm implemented is “greedy k-means++”. It differs from the vanilla k-means++ by making several trials at each sampling step and choosing the best centroid among them. ‘random’: choose n_clusters observations (rows) at random from data for the initial centroids. If an array is passed, it should be of shape (n_clusters, n_features) and gives the initial centers. If a callable is passed, it should take arguments X, n_clusters and a random state and return an initialization.
n_init‘auto’ or int, default=’auto’: Number of times the k-means algorithm is run with different centroid seeds. The final results is the best output of n_init consecutive runs in terms of inertia. Several runs are recommended for sparse high-dimensional problems. When n_init=’auto’, the number of runs depends on the value of init: 10 if using init=’random’ or init is a callable; 1 if using init=’k-means++’ or init is an array-like.
n_clustersint, default=5: The number of clusters to form as well as the number of centroids to generate.