TimeSeriesKMeansTslearn

K-means clustering for time-series data, from tslearn.

Quickstart

from sktime.clustering.k_means import TimeSeriesKMeansTslearn

estimator = TimeSeriesKMeansTslearn(n_clusters=3, max_iter=50, tol=1e-06, n_init=1, metric='euclidean', max_iter_barycenter=100, metric_params=None, n_jobs=None, dtw_inertia=False, verbose=0, random_state=None, init='random')

Parameters(12)

n_clustersint (default: 3)

Number of clusters to form.

max_iterint (default: 50)

Maximum number of iterations of the k-means algorithm for a single run.

tolfloat (default: 1e-6)

Inertia variation threshold. If at some point, inertia varies less than this threshold between two consecutive iterations, the model is considered to have converged and the algorithm stops.

n_initint (default: 1)

Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.

metric{“euclidean”, “dtw”, “softdtw”} (default: “euclidean”)

Metric to be used for both cluster assignment and barycenter computation. If “dtw”, DBA is used for barycenter computation.

max_iter_barycenterint (default: 100)

Number of iterations for the barycenter computation process. Only used if metric="dtw" or metric="softdtw".

metric_paramsdict or None (default: None)

Parameter values for the chosen metric. For metrics that accept parallelization of the cross-distance matrix computations, n_jobs key passed in metric_params is overridden by the n_jobs argument.

n_jobsint or None, optional (default=None)

The number of jobs to run in parallel for cross-distance matrix computations. Ignored if the cross-distance matrix cannot be computed using parallelization. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See scikit-learns’ Glossary for more details.

dtw_inertia: bool (default: False)

Whether to compute DTW inertia even if DTW is not the chosen metric.

verboseint (default: 0)

If nonzero, print information about the inertia while learning the model and joblib progress messages are printed.

random_stateinteger or numpy.RandomState, optional

Generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.

init{‘k-means++’, ‘random’ or an ndarray} (default: ‘random’)

Method for initialization: ‘k-means++’: use k-means++ heuristic. See scikit-learn’s k_init_ for more. ‘random’: choose k observations (rows) at random from data for the initial centroids. If an ndarray is passed, it should be of shape (n_clusters, ts_size, d) and gives the initial centers.