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 .