K means clustering: K-means K-means is an

K-means K-means is an unsupervised learning method for clustering data points. The algorithm iteratively divides data points into K clusters by minimizing the variance in each cluster . Here, we will show you how to estimate the best value for K using the elbow method, then use K-means clustering to group the data points into clusters. What is k-means clustering? K-means clustering is an unsupervised learning algorithm used for data clustering, which groups unlabeled data points into groups or clusters. It is one of the most popular clustering methods used in machine learning. K-means clustering is a popular method for grouping data by assigning observations to clusters based on proximity to the cluster’s center . This article explores k-means clustering, its importance, applications, and workings, providing a clear understanding of its role in data analysis. k-means clustering is a method of vector quantization , originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid).