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@@ -93,7 +93,7 @@ You should use some big data solutions in face recognition when the data becomes
 
 On the other hand, approximate nearest neighbor (a-nn) algorithm reduces the time complexity dramatically. [Spotify Annoy](https://sefiks.com/2020/09/16/large-scale-face-recognition-with-spotify-annoy/), [Facebook Faiss](https://sefiks.com/2020/09/17/large-scale-face-recognition-with-facebook-faiss/) and [NMSLIB](https://sefiks.com/2020/09/19/large-scale-face-recognition-with-nmslib/) are amazing a-nn libraries. Besides, [Elasticsearch](https://sefiks.com/2020/11/27/large-scale-face-recognition-with-elasticsearch/) wraps an a-nn algorithm and it offers highly scalability feature. You should run deepface within those a-nn frameworks if you have really large scale data sets. Those libraries come with high speed but they don't guarantee to find the closest ones always in contrast to k-nn algorithm run in nosql databases.
 
-Finally, graph databases offer to discover relations hard to find. [Neo4j](https://sefiks.com/2021/04/03/deep-face-recognition-with-neo4j/) is a pretty graph database exploring indirect relations between facial images.
+Finally, graph databases offer to discover relations hard to find. [Neo4j](https://sefiks.com/2021/04/03/deep-face-recognition-with-neo4j/) is a pretty graph database exploring indirect relations between facial images. If you plan to apply unsupervised learning and clustering, then graph databases will work well.
 
 Here, you can find some implementation demos of deepface with a-nn libraries: [`Elasticsearch`](https://youtu.be/i4GvuOmzKzo) and [`Spotify Annoy`](https://youtu.be/Jpxm914o2xk); key-value stores: [`Redis`](https://youtu.be/eo-fTv4eYzo), [`Cassandra`](https://youtu.be/VQqHs6-4Ylg); and graph databases: [`Neo4j`](https://youtu.be/X-hB2kBFBXs).