Affinity Propagation is a clustering algorithm that aims to identify exemplar data points within a dataset based on their "affinity" or similarity to other data points. It does not require the pre-specification of the number of clusters and can automatically determine the optimal number based on the input data.
The algorithm uses a message-passing technique to iteratively update the belief or responsibility of each data point, representing its preference to be an exemplar, and its availability to choose other data points as exemplars. The algorithm converges when the exemplars no longer change.
Customer Segmentation: Affinity Propagation can be used to identify distinct groups of customers based on their purchasing behaviors, demographics, or preferences. This information can be valuable for targeted marketing campaigns or personalized recommendations.
Image Clustering: It can be used to cluster images based on visual features or a combination of visual and textual features. This can help in organizing large image databases or identifying similar images for image retrieval tasks.
Fraud Detection: Affinity Propagation can be applied to identify anomalous patterns or clusters of fraudulent activities within a dataset, such as credit card transactions or insurance claims. This can help in detecting and preventing fraudulent behavior.
Scikit-learn: Affinity Propagation Documentation
Machine Learning Mastery: Affinity Propagation for Clustering in Python
Towards Data Science: A Gentle Introduction to Affinity Propagation Clustering
Please note that the expertise rankings are subjective and can vary over time, but the provided individuals have notable contributions and experience with Affinity Propagation.