Affinity propagation

In statistics and data mining, affinity propagation (AP) is a clustering algorithm based on the concept of "message passing" between data points. Unlike clustering algorithms such as k-means or k-medoids, affinity propagation does not require the number of clusters to be determined or estimated before running the algorithm. Similar to k-medoids, affinity propagation finds "exemplars", members of the input set that are representative of clusters.

Let x₁ through x_n be a set of data points, with no assumptions made about their internal structure, and let s be a function that quantifies the similarity between any two points, such that s(x_i, x_j) > s(x_i, x_k) iff x_i is more similar to x_j than to x_k. For this example, the negative squared distance of two data points was used i.e. for points x_i and x_k, ${\displaystyle s(i,k)=-\left\|x_{i}-x_{k}\right\|^{2}}$

...
Wikipedia