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K-medoids

K-medoids is a clustering algorithm that works by finding k data points (called medoids) such that the total distance between each data point and the closest medoid is minimal.

kmedoids
kmedoids!
KmedoidsResult

[References](@id kmedoid_refs)

  1. Teitz, M.B. and Bart, P. (1968). Heuristic Methods for Estimating the Generalized Vertex Median of a Weighted Graph. Operations Research, 16(5), 955–961. doi:10.1287/opre.16.5.955
  2. Schubert, E. and Rousseeuw, P.J. (2019). Faster k-medoids clustering: Improving the PAM, CLARA, and CLARANS Algorithms. SISAP, 171-187. doi:10.1007/978-3-030-32047-8_16