# ML Wiki

## K-Medoids

K-Medoids is a variation of K-Means clustering algorithm

Algorithm:

• use a set of points from the original data set as anchors ("medoids")
• then build clusters around them
• each item is assigned to its closest representation from the data set
• iterative approach

Objective

• $J$: average similarity of each item to its centroid

• require many iterations to converge
• so it's slow: it's slow to compute $J$
• doesn't always work well for sparse data
• e.g. for text, not many docs have lots of terms in common
• so similarities between such pairs are small and noisy
• a single medoid may not contain all needed information to build a cluster around it