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K-Means Clustering in Unsupervised Learning by ariw200201 is a document available to read on EtoBox.

K-means clustering is an unsupervised learning technique that groups unlabeled data points into k clusters based on their similarities. It aims to partition the data space into Voronoi cells by minimizing the within-cluster sum of squares from the cluster means. The k-means algorithm initializes cluster centroids and then alternates between assigning data points to the nearest centroid and recalculating the centroid means. It converges when cluster assignments no longer change, but only finds a local minimu

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ariw200201

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EN