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Iterative K-Means Clustering is a widely used unsupervised machine learning algorithm primarily employed for partitioning a dataset into distinct clusters based on feature similarity. The main objective of the algorithm is to group data points such that those within the same cluster are more similar to each other than to those in different clusters. In the context of smart technologies, both hardware and software systems often generate large volumes of data, and K-Means helps in organizing and analyzing this data efficiently for tasks like predictive maintenance, resource optimization, and user behavior analysis. The algorithm begins with the selection of K, which _____ ____ ____ ___ ____.
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