diff --git a/metainfo.json b/metainfo.json index 8e77b67de961d8e6b5e8fdc817264cea4e4640c4..a803ab1c6d924e395c0f6e7119ba5be161674bf9 100644 --- a/metainfo.json +++ b/metainfo.json @@ -5,33 +5,32 @@ ], "email": "sbailo@fhi-berlin.mpg.de", "title": "Introduction to clustering", - "description": "In this tutorial we introduce to the most popular clustering algorithms. We focus on partitioning, hierarchical and density-based clustering algorithms, and methods are tested on artificial datasets of increasing complexity", + "description": "In this tutorial, we introduce to the most popular clustering algorithms. We focus on partitioning, hierarchical and density-based clustering algorithms. The methods are tested on synthetic datasets of increasing complexity", "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-clustering-tutorial", "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/clustering_tutorial.ipynb", "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/clustering_tutorial.ipynb", - "updated": "2020-12-2", + "updated": "2020-12-02", "flags":{ "featured": true, "top_of_list": false }, "labels": { - "application_keyword": [ - "k-means", - "Hierarchical clustering", - "DBSCAN", - "HDBSCAN" - ], "application_section": [ "Tutorials for artificial-intelligence methods" ], "application_system": [ - "Artificial dataset" + "Synthetic data" ], "category": [ "beginner_tutorial" ], "data_analytics_method": [ - "Clustering" + "Unsupervised learning", + "Clustering", + "k-means", + "Hierarchical clustering", + "DBSCAN", + "HDBSCAN" ], "platform": [ "jupyter"