# 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: {% embed url="" %} 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. ![Looking at this cluster of players who are similar to Chad Prince](/files/-MYsiuPP1pbLOyZPX98I) 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. ![Groups of players like Simon Elliott](/files/-MYsjoY4PUQj_I6i2x_f) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://learn.getvectorai.com/vector-search/100-clustering.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.