Clustering Visualization V-Team

Advantages of Affinity Propagation over K-Means: No need to specify the number of clusters beforehand: Unlike K-Means, Affinity Propagation does not require the user to specify the number of clusters beforehand. This makes it more flexible and suitable for datasets where the number of clusters is unknown or difficult to determine. Ability to identify exemplars: Affinity Propagation identifies exemplars, which are data points that are representative of the cluster. This can be useful in identifying key data points or representatives of a group. Better for non-spherical clusters: K-Means assumes that clusters are spherical in shape, which is not always true. Affinity Propagation does not make this assumption and can handle non-spherical clusters better. Robust to noise and outliers: Affinity Propagation is more robust to noise and outliers in the data as it takes into account all data points in the clustering process. Disadvantages of Affinity Propagation over K-Means: Computationally ex...