The goal of k-means clustering is to partition a given dataset into k distinct clusters in order to identify underlying patterns or groupings within the data. This unsupervised learning algorithm assigns each data point to the cluster with the nearest mean value, hence the name "k-means." The algorithm aims to minimize the within-cluster variance, or