# K-means

You can use the clustering algorithm, k-means clustering, to cluster data points into k different groups based on similarities between the data points.

You can use the clustering algorithm, *k-means clustering*, to cluster data points into *k* different groups based on similarities between the data points.

The purpose of k-means is to partition *n* observations into *k* clusters. Through this partitioning, k-means assigns each observation to the cluster with the nearest mean. That nearest mean is also known as the *cluster center*.

For a complete example of how to use k-means on a table in Vertica, see Clustering data using k-means .