K-Means Clustering using sklearn - The Security Buddy

K-means clustering is an unsupervised learning algorithm that can be used for solving clustering problems in machine learning. K-means clustering takes a bunch of unlabeled data and groups them into k clusters. The clustering is done so that each point belongs to its nearest cluster center. And we usually use the Manhattan distance or Euclidean distance to measure the distance between each point and cluster centers. In our previous article, we discussed how k-means clustering works.