Briefly describe the Iterative K-means clustering algorithm?

The Iterative K-means clustering algorithm is a popular unsupervised machine learning technique used to partition a set of data points into a predefined number of clusters, denoted as K. The objective is to group data such that points within the same cluster are more similar to each other than to those in other clusters. The algorithm works through the following iterative steps:

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