Information Theory
k-means clustering is an unsupervised machine learning algorithm used to partition a dataset into k distinct clusters based on feature similarity. Each cluster is represented by its centroid, which is the mean of the data points within that cluster. This technique is widely applied in vector quantization, where the goal is to reduce the dimensionality of data while preserving its structure.
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