This example demonstrates a K-means clustering implementation written in DAX, showing how to group items into clusters based on multiple numeric attributes. The code computes a transformed table using `SUMMARIZECOLUMNS` to aggregate attribute values per item, then passes that table to a custom `KMeansClustering` function. The function assigns each item a `ClusterId` while preserving the original item labels. The visualization highlights the resulting clusters, with the DAX code serving as the core analytical engine rather than relying on a JavaScript library. The source is a gist by Hugoberry, and the rendering approach is not visible here, so the focus remains on the clustering logic and the data transformation pipeline.