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The Iterative K-Means Clustering Algorithm is a widely used unsupervised machine learning technique that is designed to partition a dataset into a predetermined number of clusters, denoted as $k$. The primary objective of this algorithm is to group similar data points together while ensuring that points in different clusters are as distinct as possible. The method is iterative in nature, which means it refines cluster assignments repeatedly until a convergence criterion is met. This makes it highly suitable for applications in smart _____ ____ ___ __________ ________ ______ _______ ________.
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