IterativeVotingClustering

class tsml_eval.estimators.clustering.consensus.IterativeVotingClustering(clusterers=None, init='plus', n_clusters=8, max_iterations=500, random_state=None)

IVC (Iterative Voting Clustering) Consensus Clusterer.

IVC is a consensus clustering algorithm that combines the results of multiple base clusterers to find a consensus clustering. It iteratively refines cluster assignments based on a majority voting scheme.

Parameters:

clustererslist of clusterers, default=None

A list of clusterers to use in the ensemble. If None, defaults to 5 KMeans clusterers.

init{‘plus’, ‘random’, ‘aligned’}, default=’plus’

The method used to initialize the cluster centers. ‘plus’ uses the k-means++ initialization method, ‘random’ uses random selection, and ‘aligned’ uses a method that aligns the cluster assignmeds from the base clusterers.

n_clustersint, default=8

The number of clusters to form.

max_iterationsint, default=500

The maximum number of iterations to perform.

random_stateint, default=None

The seed for random number generation.

Examples

>>> from tsml_eval.estimators.clustering.consensus.ivc import (
...     IterativeVotingClustering
... )
>>> from sklearn.datasets import load_iris
>>> from sklearn.metrics import rand_score
>>> iris = load_iris()
>>> ivc = IterativeVotingClustering(n_clusters=3, random_state=0)
>>> ivc.fit(iris.data)
IterativeVotingClustering(...)
>>> rand_score(iris.target, ivc.labels_)
0.8737360178970918

Attributes:

labels_ndarray of shape (n_instances,)

Labels of each point from the last fit.

Methods

fit(X[, y])	Fit model to X using IVC.
fit_predict(X[, y])	Perform clustering on X and returns cluster labels.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict cluster labels for X.
set_params(**params)	Set the parameters of this estimator.

fit(X, y=None)

Fit model to X using IVC.

predict(X)

Predict cluster labels for X.

fit_predict(X, y=None, **kwargs)

Perform clustering on X and returns cluster labels.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input data.

yIgnored

Not used, present for API consistency by convention.

**kwargsdict

Arguments to be passed to fit.

Added in version 1.4.

Returns:

labelsndarray of shape (n_samples,), dtype=np.int64

Cluster labels.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.