K-Means
K-Means is a clustering algorithm that partitions n observations into k clusters, where each observation belongs to the cluster with the nearest mean. It is an unsupervised learning algorithm that aims to group similar data points together based on their distance to the centroid of the cluster. The algorithm starts by randomly selecting k centroids, and then iteratively assigns each data point to the nearest centroid and updates the centroid based on the mean of the data points assigned to it. The algorithm stops when the centroids no longer move or a maximum number of iterations is reached.
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