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K-means clustering is a popular unsupervised machine learning algorithm used to partition a dataset into K distinct, non-overlapping subsets or clusters. The algorithm aims to minimize the variance within each cluster, thereby maximizing the separation between different clusters. This method is particularly useful for exploratory data analysis and pattern recognition.
The K-means algorithm operates through an iterative process. It starts by initializing K centroids, which are
One of the key advantages of K-means clustering is its simplicity and efficiency, making it suitable for
K-means clustering has a wide range of applications, including market segmentation, image compression, and anomaly detection.