Affinity Propagation Clustering Algorithm

An Affinity Propagation Clustering Algorithm is a clustering algorithm that iteratively updates messages between data points until convergence.

• AKA: Affinity Propagation, AP.

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References

2015

• (Fujiwara et al., 2015) ⇒ Yasuhiro Fujiwara, Makoto Nakatsuji, Hiroaki Shiokawa, Yasutoshi Ida, and Machiko Toyoda. (2015). “Adaptive Message Update for Fast Affinity Propagation.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783280
  • QUOTE: Affinity Propagation is a clustering algorithm used in many applications. It iteratively updates messages between data points until convergence. The message updating process enables Affinity Propagation to have higher clustering quality compared with other approaches. However, its computation cost is high; it is quadratic in the number of data points.

2009

• (Zhang et al., 2009) ⇒ Xiangliang Zhang, Cyril Furtlehner, Julien Perez, Cecile Germain-Renaud, and Michèle Sebag. (2009). “Toward Autonomic Grids: Analyzing the Job Flow with Affinity Streaming.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557126

2007

• (Frey & Dueck, 2007) ⇒ Brendan J. Frey, and Delbert Dueck. (2007). “Clustering by Passing Messages Between Data Points.” In: Science, 315(5814). doi:10.1126/science.1136800.

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