K-means is a clustering algorithm used in the field of unsupervised learning. The objective of the algorithm is to group a set of data into K clusters, where K is a predefined number of clusters. The algorithm starts by selecting K centroids at random and assigning each data point to the nearest centroid. Then, the algorithm recalculates the centroids as the average of all data points assigned to each centroid, and repeats the process of assigning and recalculating centroids until convergence is reached and the centroids no longer change position significantly. As a result, the data space is divided into K Voronoi cells (one per centroid), and each input observation can be associated with the nearest centroid. The K-means algorithm is widely used in customer segmentation, text classification and image processing, among other applications.
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