# AFFINITY PROPAGATION CLUSTERING ALGORITHM

This clustering method groups data in a fundamentally different way than the others. Each data point connects with all of the other data points to determine how similar they are, which begins to show the data clusters. In the initialization settings, you do not need to inform this algorithm how many clusters to expect. As messages are transmitted between data points, groups of data known as exemplars are discovered and used to represent clusters. An exemplar is discovered when the data points communicate with one another and reach an agreement on which data point best reflects a cluster. When you don't know how many clusters to expect, such as in a computer vision task, this is a good place to start.

# THE MAIN CODE OF AFFINITY PROPAGATION CLUSTERING ALGORITHM

# Full Code Of Implementing Affinity Propagation Clustering Algorithm

Practicing Affinity Propagation clustering algorithm:

`from sklearn.cluster import AffinityPropagation`