K-Means clustering is a widely used unsupervised machine learning algorithm for partitioning the data into K clusters based on their similarities. The algorithm has been extensively applied in various fields, including data mining, image processing, and bioinformatics. This paper provides a comprehensive review of the K-Means clustering algorithm, its variants, and applications. We discuss the basic concepts, advantages, and disadvantages of the algorithm, as well as its extensions and improvements. We also present some real-world applications of K-Means clustering in different domains.
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