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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 points in the feature space representing the center of each cluster. The algorithm then assigns each data point to the nearest centroid, forming K clusters. After all points are assigned, the centroids are recalculated as the mean of all points in each cluster. This process is repeated until the centroids no longer change significantly, or a predefined number of iterations is reached.

One of the key advantages of K-means clustering is its simplicity and efficiency, making it suitable for large datasets. However, it requires the number of clusters K to be specified in advance, which can be a limitation. Additionally, the algorithm is sensitive to the initial placement of centroids and can converge to local optima rather than the global optimum. To mitigate these issues, techniques such as multiple initializations and the use of domain knowledge to choose K can be employed.

K-means clustering has a wide range of applications, including market segmentation, image compression, and anomaly detection. Its versatility and computational efficiency make it a valuable tool in the data scientist's toolkit.