apcluster: Affinity Propagation Clustering. The apcluster package implements Frey’s and Dueck’s Affinity Propagation clustering in R. The algorithms are largely analogous to the Matlab code published by Frey and Dueck. The package further provides leveraged affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing 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) ioport
  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) ioport
  11. Kong, W.W.; Ranganath, Surendra: Towards subject independent continuous sign language recognition: a segment and merge approach (2014) ioport
  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) ioport
  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) ioport
  16. Zhang, Yongqin; Liu, Jiaying; Li, Mading; Guo, Zongming: Joint image denoising using adaptive principal component analysis and self-similarity (2014) ioport
  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) ioport
  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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