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K-Means clustering is an unsupervised machine learning technique widely used in Data Science and Big Data for grouping similar data points into clusters based on their features. It is particularly useful when dealing with large datasets where natural groupings exist but are not labeled. The main idea is to partition the dataset into K distinct clusters such that each data point belongs to the cluster with the nearest mean, which serves as the cluster centroid. The algorithm iteratively minimizes the within-cluster sum of squares (WCSS), ensuring that points in the same cluster are as similar as possible while being distinct from points in other ____ ___ ____ ________ _______ ____ __________ ________ ___ _______ ___ _________.
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