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Think about you stroll right into a busy espresso store with lots of people, and also you’re tasked with grouping everybody into smaller, extra manageable teams primarily based on how they’re sitting. You need to create teams the place persons are shut to one another, however every group ought to be distinct and separate.
That is the essential concept behind Okay-Means Clustering, probably the most extensively used unsupervised studying algorithms. Similar to grouping folks in a espresso store primarily based on their seating, Okay-Means Clustering helps us group knowledge factors into distinct clusters primarily based on their similarity.
Okay-Means Clustering is a means of grouping knowledge into Okay clusters (therefore the identify “Okay-Means”). The aim is to divide a set of knowledge factors into Okay clusters, the place every knowledge level belongs to the cluster with the closest imply (or middle). It’s known as unsupervised studying as a result of we don’t present any labels or classes — simply the information itself.
Consider it like this: You’re attempting to kind a bunch of individuals into clusters, however you don’t know what teams they belong to beforehand. You begin by guessing and adjusting your guesses till the teams are as significant as doable.
