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When it's better to use K-Medoids rather than K-Means? Can anybody give some examples of dataset for the same?

user9855045
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  • From Wikipedia: `It is more robust to noise and outliers as compared to k-means because it minimizes a sum of pairwise dissimilarities instead of a sum of squared Euclidean distances.` – user2974951 Jul 09 '20 at 07:40
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    You may also want to use K-medoid when you want the representers of the classes to be one of the points in your dataset. Ex : representer of a class of pictures of faces is a blurry picture for K-means and it is a real picture of someone's face in K-medoid, one of the exemples of the dataset. – TMat Jul 09 '20 at 08:00
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    The question is similar to when to use median instead of mean. Check the answers here https://stats.stackexchange.com/questions/6913/is-median-fairer-than-mean Mean is trying to minimize L2 loss and Median is trying to minimize L1 loss. https://stats.stackexchange.com/questions/34613/l1-regression-estimates-median-whereas-l2-regression-estimates-mean/34615#34615 – Haitao Du Jul 09 '20 at 08:04

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