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Understanding Medoids and Centroids by Catalina Mocanu is a document available to read on EtoBox.

What is Understanding Medoids and Centroids about?

The document discusses K-medoids clustering, comparing it to K-means clustering. K-medoids clustering uses medoids, which are actual data points from the dataset, rather than centroids. This makes K-medoids more robust to outliers. The document outlines the K-medoids algorithm and variations like PAM and CLARA, which were developed to improve efficiency. K-medoids is more suitable than K-means when centroids cannot be defined, like for 3D trajectories or gene expression data.

Author

Catalina Mocanu

Language

EN