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Lecture18
This document summarizes a lecture on clustering algorithms in machine learning. It introduces hierarchical clustering and partitional clustering, focusing on the k-means algorithm. K-means aims to partition objects into K clusters by minimizing total squared error from cluster means. It requires specifying the number of clusters K and uses iterative reassignment to nearest cluster means to converge on a solution. The document provides examples and discusses applications of clustering in domains like bioinformatics and the traveling salesman problem.
