STDBSCAN

class STDBSCAN(eps1=0.5, eps2=10, min_samples=5, metric='euclidean', sparse_matrix_threshold=20000, frame_size=None, frame_overlap=None, n_jobs=-1)[source]

Spatio-temporal DBSCAN clustering.

Implementation of STDBSCAN by Birant et al [1]. Partially based on the implementation of Cakmak et al [3].

Clusters data based on specified spatial and temporal proximity thresholds.

Assumes that all variables are spatial coordinates.

Parameters:

eps1float, default=0.5: Maximum spatial distance for points to be considered related.
eps2float, default=10: Maximum temporal distance for points to be considered related [1].
min_samplesint, default=5: Minimum number of samples to form a core point.
metricstr, default=’euclidean’: Distance metric to use; options include ‘euclidean’, ‘manhattan’, ‘chebyshev’, etc.
sparse_matrix_thresholdint, default=20_000: Sets the limit on the number of samples for which the algorithm can efficiently compute distances with a full matrix approach. Datasets exceeding this threshold will be handled using sparse matrix methods.
frame_sizefloat or None, default=None: If not None the dataset is split into frames [2, 3]; The frame_size is the number of time points in a frame.
frame_overlapfloat or None, default=eps2: If frame_size is set - there will be an overlap between the frames to merge the clusters afterward [2, 3]; Only used if frame_size is not None.
n_jobsint or None, default=-1: Number of parallel jobs for distance computation; -1 uses all cores.

Attributes:

labels_np.ndarray of shape (n_samples,): Cluster labels for each point; noise is labeled as -1.

References

[1]

Birant, D., & Kut, A. “ST-DBSCAN: An algorithm for clustering spatial-temporal data.” Data Knowl. Eng., vol. 60, no. 1, pp. 208-221, Jan. 2007, doi: [10.1016/j.datak.2006.01.013](https://doi.org/10.1016/j.datak.2006.01.013).

[2]

Peca, I., Fuchs, G., Vrotsou, K., Andrienko, N., and Andrienko, G., “Scalable Cluster Analysis of Spatial Events” 2012, The Eurographics Association doi: [10.2312/PE/EUROVAST/EUROVA12/019-023](https://doi.org/10.2312/PE/EUROVAST/EUROVA12/019-023).

[3]

Cakmak, E., Plank, M., Calovi, D. S., Jordan, A., & Keim, D. “Spatio-temporal clustering benchmark for collective animal behavior.” ACM, Nov. 2021, pp. 5-8. doi: [10.1145/3486637.3489487](https://doi.org/10.1145/3486637.3489487).

Examples

>>> from sktime.clustering.spatio_temporal import STDBSCAN
>>> from sktime.clustering.utils.toy_data_generation._make_moving_blobs import (
... make_moving_blobs)
>>> X, y_true = make_moving_blobs(n_times=20)
>>> st_dbscan = STDBSCAN(
...     eps1=0.5, eps2=3, min_samples=5, metric="euclidean", n_jobs=-1
... )
>>> st_dbscan.fit(X)
>>> predicted_labels = st_dbscan.labels_

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.