Spatio-Temporal Clustering

Movekit allows the use of many different clustering algorithms for spatio-temporal clustering of the movement data: dbscan, hdbscan, agglomerative, optics, spectral, affinitypropagation. This is done by calling the function clustering with the required algorithm. Different hyperparameters can be set for the different algorithms, for an exact description of the parameters of each algorithm see: https://github.com/dbvis-ukon/spatio-temporal-clustering/blob/main/src/st_clustering/st_clustering.py. Additionally there is the function clustering_with_splits which uses a splitting-and-merging method (data is partitioned into frames and merged afterwards) to increase performance for large datasets.

clustering(algorithm, data, **kwargs):

Clustering of spatio-temporal data.

param algorithm: Choose between ‘dbscan’, ‘hdbscan’, ‘agglomerative’, ‘optics’, ‘spectral’, ‘affinitypropagation’.

param data: DataFrame to perform clustering on.

return: labels as numpy array where the label in the first position corresponds to the first row of the input data.

clustering_with_splits(algorithm, data, frame_size, **kwargs):

Clustering of spatio-temporal data.

param algorithm: Choose between dbscan, hdbscan, agglomerative, optics, spectral, affinitypropagation.

param data: DataFrame to perform clustering on.

param frame_size: the dataset is partitioned into frames and merged afterwards.

return: labels as numpy array where the label in the first position corresponds to the first row of the input data.

# mkit.clustering(algorithm, data, **kwargs)
labels = mkit.clustering('dbscan', data_norm, eps1=0.05, eps2=10, min_samples=2)
#OR cluster with the splitting-and-merging method (data is partitioned into frames and merged afterwards).
labels = mkit.clustering_with_splits('dbscan', data, frame_size=20, eps1=0.05, eps2=10, min_samples=3)