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Medoid: Representative members of a data set whose average dissimilarity to all the members of the data set is minimal. Medoids are similar in concept to means or centroids, but medoids are always members of the data set. Medoids are typically used in classification algorithms to group (or cluster) similar pixels in an image using the characteristics of those pixels. One pixel is chosen from that group to be the “optimized” medoid that represents the most similarity to other pixels.

In my “NASA job” we use medoids for land classification algorithms when looking at Landsat satellite data. This is not a common statistical term, but an interesting one.

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