dtaidistance.clustering.medoids¶
Time series clustering using medoid-based methods.
author: | Wannes Meert |
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copyright: | Copyright 2020 KU Leuven, DTAI Research Group. |
license: | Apache License, Version 2.0, see LICENSE for details. |
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class
dtaidistance.clustering.medoids.
KMedoids
(dists_fun, dists_options, k=None, initial_medoids=None, show_progress=True)¶ KMedoids using the PyClustering package.
Novikov, A., 2019. PyClustering: Data Mining Library. Journal of Open Source Software, 4(36), p.1230. Available at: http://dx.doi.org/10.21105/joss.01230.
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fit
(series)¶
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plot
(filename=None, axes=None, ts_height=10, bottom_margin=2, top_margin=2, ts_left_margin=0, ts_sample_length=1, tr_label_margin=3, tr_left_margin=2, ts_label_margin=0, show_ts_label=None, show_tr_label=None, cmap='viridis_r', ts_color=None)¶
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class
dtaidistance.clustering.medoids.
Medoids
(dists_fun, dists_options, k, show_progress=True)¶ Parameters: - dists_fun –
- dists_options –
- show_progress –
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plot
(filename=None, axes=None, ts_height=10, bottom_margin=2, top_margin=2, ts_left_margin=0, ts_sample_length=1, tr_label_margin=3, tr_left_margin=2, ts_label_margin=0, show_ts_label=None, show_tr_label=None, cmap='viridis_r', ts_color=None)¶