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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 technologies, both in hardware and software, such as sensor data analysis, predictive maintenance, customer segmentation, and smart IoT systems. The algorithm starts with the initialization step, where $k$ centroids are selected. These centroids can be chosen randomly from the dataset or _________ _____ _________ ______ ___.
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