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. 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). K-Means clustering can be used to analyze gene expression data to identify different groups of genes that are co-regulated or co-expressed. This technique is widely used in bioinformatics applications, such as drug discovery, disease diagnosis, and personalized medicine. 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.
