Question

What is the difference between KNN and k-means clustering? Write in detail.

Answer 1

A supervised classification algorithm, K-Nearest Neighbors is used to classify data, whereas k-means clustering is used to cluster data in an unsupervised manner. However, while the mechanics may appear to be comparable at first glance, what this truly means is that in order for K-Nearest Neighbors to operate, you must first have labeled data into which you want to classify an unlabeled point before the algorithm can work (thus the nearest neighbor part). It is only necessary to provide a set of unlabeled points and a threshold for K-means clustering to work: the algorithm will take the unlabeled points and progressively learn how to classify them into groups by computing the mean of the distance between various points.

The key distinction here is that KNN requires labeled points and is therefore supervised learning, whereas k-means does not require labeled points and is therefore unsupervised learning.