K means clustering: 1

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 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). Applications kmeans algorithm is very popular and used in a variety of applications such as market segmentation, document clustering , image segmentation and image compression, etc. The goal usually when we undergo a cluster analysis is either: Get a meaningful intuition of the structure of the data we’re dealing with. Cluster -then-predict where different models will be built for different subgroups if we believe there is a wide variation in the behaviors of different subgroups. An example ... 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.