K-Means Clustering Explained

K-Means clustering groups data points into a fixed number of clusters based on their similarity.

The process starts by placing a chosen number of cluster centers randomly. Each data point then joins the nearest center, forming an initial set of groups.

From there, the algorithm repeatedly recalculates each cluster's center based on its current members, and reassigns points if a closer cluster appears. This continues until the clusters stop changing.

• The letter K represents the number of clusters you want to form.
• Points within the same cluster are more similar to each other than to points in other clusters.
• Choosing the right value of K often requires testing different options and comparing results.

Retail companies commonly use K Means to segment customers into groups like frequent buyers, occasional shoppers, and one time visitors, based purely on purchase patterns.