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K-Medoids Algorithm in Machine Learning by asdasd is a document available to read on EtoBox.

This document summarizes a lecture on clustering algorithms. It introduces the K-medoids clustering algorithm, which is similar to K-means but uses actual data points as cluster representatives rather than means. It discusses initializing the algorithm and selecting the number of clusters. Poor initialization can result in a suboptimal clustering, so initialization is important.

Author

asdasd

Language

EN