Update metainfo.json

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"