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What is Understanding Clustering Algorithms about?

K-means clustering is an unsupervised learning algorithm that groups unlabeled data points into a specified number (k) of clusters. It works by [1] randomly initializing k cluster centers and then [2] iteratively assigning data points to their closest cluster center and recalculating the cluster centers until cluster membership stabilizes. The goal is to minimize intra-cluster variance, resulting in high similarity between points within each cluster and high dissimilarity between points of different cluster

Author

SyedDabeerAli

Language

EN