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Iterative K-means clustering is a popular unsupervised machine learning algorithm used to group data points into clusters based on their similarity. In the context of smart technologies, both hardware and software systems generate large volumes of data, such as sensor readings, device usage logs, and user behavior patterns. K-means clustering helps in analyzing and organizing this data to make systems more intelligent and adaptive. The process begins by selecting the number of clusters, denoted as *K*, which represents how many groups the dataset should be divided into. The choice of K can be based on prior knowledge or determined using methods such as the elbow method, which identifies the point at which adding more clusters does not significantly improve clustering performance. After ___ ___ _________ _____ _________ ________ __________ ________ ______.
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