TimeSeriesKvisibility

TimeSeriesKvisibility

class TimeSeriesKvisibility(n_clusters=5, init='k-means++', n_init=4)[source]

Kvisibility for time series clustering.

kvisibility is a time series clustering technique based on visibility graphs. The algorithm is based on the transformation of the time series into graphs, and with metrics of the created graphs create a clustering with Kmeans.

Based on the paper [1].

Parameters:

n_clustersint, default=5

The number of clusters to form as well as the number of centroids to generate.

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.

Attributes:

core_sample_indices_ndarray of shape (n_core_samples,)

Indices of core samples.

components_ndarray of shape (n_core_samples, n_features)

Copy of each core sample found by training.

labels_ndarray of shape (n_samples)

Cluster labels for each point in the dataset given to fit(). Noisy samples are given the label -1.

References

[1]

https://www.aimspress.com/article/doi/10.3934/math.20241687

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X[, y])	Fit time series clusterer to training data.
fit_predict(X[, y])	Compute cluster centers and predict cluster index for each time series.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
predict(X[, y])	Predict the closest cluster each sample in X belongs to.
predict_proba(X)	Predicts labels probabilities for sequences in X.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
score(X[, y])	Score the quality of the clusterer.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.