Affinity propagation (AP) is a clustering algorithm that has been introduced by Brendan J. Frey and Delbert Dueck. The authors themselves describe affinity propagation as follows: ”An algorithm that identifies exemplars among data points and forms clusters of data points around these exemplars. It operates by simultaneously considering all data point as potential exemplars and exchanging messages between data points until a good set of exemplars and clusters emerges.” AP has been applied in various fields recently, among which bioinformatics is becoming increasingly important. Frey and Dueck have made their algorithm available as Matlab code. Matlab, however, is relatively uncommon in bioinformatics. Instead, the statistical computing platform R has become a widely accepted standard in this field. In order to leverage affinity propagation for bioinformatics applications, we have implemented affinity propagation as an R package. Note, however, that the given package is in no way restricted to bioinformatics applications. It is as generally applicable as Frey’s and Dueck’s original Matlab code. The package further implements leveraged affinity propagation, exemplar-based agglomerative clustering, and various tools for visual analysis of clustering results.

References in zbMATH (referenced in 57 articles )

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  1. He, Xuan-sen; He, Fan; Cai, Wei-hua: Underdetermined BSS based on $K$-means and AP clustering (2016)
  2. Li, Ying; He, Ye; Zhang, Yu: Analyzing gene expression time-courses based on multi-resolution shape mixture model (2016)
  3. Santi, Éverton; Aloise, Daniel; Blanchard, Simon J.: A model for clustering data from heterogeneous dissimilarities (2016)
  4. Shroff, Nitesh; Anirudh, Rushil; Chellappa, Rama: Summarization and search over geometric spaces (2016)
  5. Brusco, Michael J.; Steinley, Douglas: Affinity propagation and uncapacitated facility location problems (2015)
  6. Khalid, Shehzad; Razzaq, Shahid: TOBAE: a density-based agglomerative clustering algorithm (2015)
  7. Meng, Jun; Li, Rui; Luan, Yushi: Classification by integrating plant stress response gene expression data with biological knowledge (2015)
  8. Nellore, Abhinav; Ward, Rachel: Recovery guarantees for exemplar-based clustering (2015)
  9. Panagiotakis, Costas: Point clustering via voting maximization (2015)
  10. Cagnina, Leticia; Errecalde, Marcelo; Ingaramo, Diego; Rosso, Paolo: An efficient particle swarm optimization approach to cluster short texts (2014)
  11. Kong, W.W.; Ranganath, Surendra: Towards subject independent continuous sign language recognition: a segment and merge approach (2014)
  12. Li, Jia; Tian, Yonghong; Huang, Tiejun: Visual saliency with statistical priors (2014)
  13. Richarz, Jan; Vajda, Szilard; Grzeszick, Rene; Fink, Gernot A.: Semi-supervised learning for character recognition in historical archive documents (2014)
  14. Sanchez, Mauricio A.; Castillo, Oscar; Castro, Juan R.; Melin, Patricia: Fuzzy granular gravitational clustering algorithm for multivariate data (2014)
  15. Tzortzis, Grigorios; Likas, Aristidis: The MinMax $k$-Means clustering algorithm (2014)
  16. Zhang, Yongqin; Liu, Jiaying; Li, Mading; Guo, Zongming: Joint image denoising using adaptive principal component analysis and self-similarity (2014)
  17. Zhuang, Yi; Jiang, Nan; Wu, Zhiang; Li, Qing; Chiu, Dickson K.W.; Hu, Hua: Efficient and robust large medical image retrieval in mobile cloud computing environment (2014)
  18. Lipovetsky, Stan: Finding cluster centers and sizes via multinomial parameterization (2013)
  19. Malliaros, Fragkiskos D.; Vazirgiannis, Michalis: Clustering and community detection in directed networks: a survey (2013)
  20. Rahim, Mehdi; Bellemare, Marc-Emmanuel; Bulot, Rémy; Pirró, Nicolas: A diffeomorphic mapping based characterization of temporal sequences: application to the pelvic organ dynamics assessment (2013)

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