Is there a connection between affinity propagation and other methods, like hierarchical agglomerative clustering and spectral clustering (normalized cuts)?
Spectral clustering (normalized cuts) identifies clusters in a way that can be viewed as passing messages between data points, but that method does not identify an exemplar for each cluster. Affinity propagation can be viewed as a spectral clustering algorithm that requires each cluster to vote for a good exemplar from within its data points. Hierarchical agglomerative clustering starts with every data point as its own cluster and then recursively merges pairs of clusters, but that method makes hard decisions that can cause it to get stuck in poor solutions. Affinity propagation can be viewed as a version of hierarchical clustering that makes soft decisions so that it is free to hedge its bets when forming clusters.
Related Questions
- Is there a connection between affinity propagation and other methods, like hierarchical agglomerative clustering and spectral clustering (normalized cuts)?
- Can affinity propagation be viewed as just a good way to initialize standard methods, such as k-centers clustering?
- Could affinity propagation be used to obtain a hierarchical clustering of the data?