K-Means Clustering is a popular unsupervised machine learning algorithm used for partitioning a dataset into K distinct clusters based on feature similarity. The algorithm operates by initializing K centroids, which represent the center of each cluster. Each data point is then assigned to the nearest centroid, forming clusters. The centroids are recalculated as the mean of all points assigned to each cluster, and this process is iterated until the centroids no longer change significantly, indicating that convergence has been reached. Mathematically, the objective is to minimize the within-cluster sum of squares, defined as:
where is the set of points in cluster and is the centroid of cluster . K-Means is widely used in applications such as market segmentation, social network analysis, and image compression due to its simplicity and efficiency. However, it is sensitive to the initial placement of centroids and the choice of K, which can influence the final clustering outcome.
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