🔍Clustering
Introduction To Clustering
Assumed Knowledge: Vectors, Vector Search Target Audience: Data scientist, Vector enthusiasts, Business analysts, Executives Reading Time: 3 minutes
Clustering: Interpret these vectors and your data to different buckets and interpret them more easily when combined with aggregation. Clustering provides a simple way to group vectors and metadata so that those with similar properties can be easily interpreted.
When we cluster, we can identify the key attributes and have a new way to observe our data.
If you are interested in an example of clustering, take a quick look at our playground:
In this dataset, we grouped together similar players with similar statistics. From here, we can tell which players are actually similar and new types of players that can emerge.
We take the player closest to the center of the cluster in order to represent the cluster by name only. From there, we take the average of the cluster and note the general statistics of the group. We can make comparisons to groups of players similar to Chad Prince and compare them to players like Simon Elliott.
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