K-Means Clustering of U nsupervised Learning Algorithm Concepts of  K-Means clustering K-Means clustering is a popular unsupervised machine learning algorithm used to cluster or group similar data points together in a dataset. The algorithm works by partitioning the data into K clusters, where K is a predetermined number chosen by the user. The goal is to minimize the distance between the data points within each cluster while maximizing the distance between the clusters. Here is an example of how K-Means clustering works : Suppose we have a dataset of customer transactions, where each transaction includes the customer's age, income, and spending behaviour. We want to group customers with similar spending behaviour together for targeted marketing campaigns. We use K-Means clustering to partition the data into K clusters based on the customer's spending behaviour. The algorithm assigns each customer to a cluster based on the similarity of their spending behaviour. K-Means clust