Michaël Karpe

[This repository](https://github.com/joshlk/k-means-constrained) proposes size-constrained k-means clustering by formulating the cluster assignment step as a minimum-cost flow problem. One possible approach could be to replace the [`euclidian_distance`](https://github.com/joshlk/k-means-constrained/blob/c08076ca9b66d4d9806712650f247dbe81162e83/k_means_constrained/k_means_constrained_.py#L402) by tslearn [`cdist_dtw`](https://github.com/tslearn-team/tslearn/blob/7eceaa48f1e7b7a121f8d5f87fd34f6b1b93adcd/tslearn/metrics/dtw_variants.py#L1144) in...

> You could try hierarchical clustering with DTW and barycenters perhaps and use a post-processing algorithm on the produced dendrogram. A time-series [BisectingKMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.BisectingKMeans.html) (as now existing in scikit-learn >= 1.1.0)...

Hi @libbyh @meixingdg, I have been able to solve the SSL certificate with the following two lines at the beginning of the `TweetManager.py` file: ``` import ssl ssl._create_default_https_context = ssl._create_unverified_context...