1. Overview K-means clustering is a simple and elegant approach for partitioning a data set into K distinct, nonoverlapping clusters. To perform K-means clustering , we must first specify the desired number of clusters K ; then, the K-means algorithm will assign each observation to exactly one of the K clusters. The below figure shows the results obtained from performing K-means clustering on a simulated example consisting of 150 observations in two dimensions, using three different values of K . 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 one of the most used clustering algorithms in machine learning. In this article, we will discuss the concept, examples, advantages, and disadvantages of the k-means clustering algorithm. We will also discuss a numerical on k-means clustering to understand the algorithm in a better way. What is K-means Clustering ? K-means clustering is an unsupervised machine learning algorithm used to group a dataset into k clusters. It is an iterative algorithm that starts by randomly ... 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.
