k-Means Clustering Algorithm Demystified

K-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. K-means clustering is an unsupervised machine learning algorithm, which means it does not require any labels or classes for the data points, but instead tries to discover the inherent structure or patterns in the data. …

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