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

What is K-means Clustering in Unsupervised Learning about?

This document discusses the k-means clustering algorithm. It begins with an introduction to clustering and unsupervised learning. It then explains the k-means algorithm, which takes as input the number of clusters k and randomly assigns cluster centroids initially. It iterates between assigning examples to their closest centroids and recalculating the centroid positions until convergence. The document discusses challenges like non-separated clusters and notes that k-means can find local optima depending on

Author

sourabh

Language

EN