<ul><li>K-Means Clustering is an algorithm that groups data points based on their similarity.</li><li>The algorithm consists of four main steps: initialization, assignment, update, and repetition.</li><li>Some key use cases of K-Means Clustering include customer segmentation, market basket analysis, image compression, document clustering, anomaly detection, and genetic data analysis.</li><li>K-Means is chosen for its simplicity, versatility, and efficiency with large datasets.</li></ul>

Understanding K-Means Clustering: Unlocking the Power of Grouping in Data Science

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