Clustering

Clustering requires a free account

AboutClustering

Group similar data points automatically.Category: AI & Machine Learning

Segmenting customers by purchase behavior for targeted campaignsGrouping sensor or IoT readings into normal operating statesFinding natural product tiers from price and usage metrics

The Clustering tool is compatible with:.Run Scaling first so every feature contributes proportionally to distance calculations, then use Clustering to assign group labels you can chart or filter on afterward.
This tool applies the K-Means algorithm to your numeric columns, iteratively assigning each row to the nearest of K cluster centroids and recalculating those centroids until the groups stabilize. It's a form of unsupervised learning: there's no target column to predict, only structure to discover in how rows sit relative to each other across your chosen features. Because K-Means is distance-based, features on wildly different scales (like revenue in thousands next to a 0-1 rate) will distort the grouping unless normalized first.

Frequently Asked Questions:

What clustering approach does it use?

It groups similar data points together automatically based on their numeric feature similarity.

Do I need to specify the number of groups in advance?

You control the clustering parameters, including how many groups to look for.

A practical starting point for analysts doing customer segmentation or pattern discovery without setting up a full Python ML environment.