K-Means Clustering Algorithm
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In the field of machine learning, the K-means clustering algorithm is a widely-used unsupervised learning technique that partitions data points into distinct clusters to facilitate better analysis of their characteristics and attributes. The algorithm relies heavily on distance metrics (typically Euclidean distance) and the selection of initial centroids, requiring proper data preprocessing and parameter tuning to achieve optimal clustering results. Implementation involves iteratively assigning data points to the nearest centroid and updating centroid positions until convergence. Key functions include centroid initialization methods like random selection or K-means++, and convergence criteria based on centroid movement thresholds or maximum iterations. Additionally, K-means clustering finds extensive applications in image segmentation, text mining, and anomaly detection, demonstrating broad practical potential across various domains.
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