apcluster

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 64 articles )

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  1. Chiang, Alvin; David, Esther; Lee, Yuh-Jye; Leshem, Guy; Yeh, Yi-Ren: A study on anomaly detection ensembles (2017)
  2. Huang, Jinlong; Zhu, Qingsheng; Yang, Lijun; Cheng, Dongdong; Wu, Quanwang: QCC: a novel clustering algorithm based on quasi-cluster centers (2017)
  3. Wang, Jingya; Zhu, Xiatian; Gong, Shaogang: Discovering visual concept structure with sparse and incomplete tags (2017)
  4. He, Xuan-sen; He, Fan; Cai, Wei-hua: Underdetermined BSS based on $K$-means and AP clustering (2016) ioport
  5. Li, Ying; He, Ye; Zhang, Yu: Analyzing gene expression time-courses based on multi-resolution shape mixture model (2016)
  6. Santi, Éverton; Aloise, Daniel; Blanchard, Simon J.: A model for clustering data from heterogeneous dissimilarities (2016)
  7. Shroff, Nitesh; Anirudh, Rushil; Chellappa, Rama: Summarization and search over geometric spaces (2016)
  8. Villmann, Thomas; Kaden, Marika; Nebel, David; Bohnsack, Andrea: Similarities, dissimilarities and types of inner products for data analysis in the context of machine learning. A mathematical characterization (2016)
  9. Brusco, Michael J.; Steinley, Douglas: Affinity propagation and uncapacitated facility location problems (2015)
  10. Khalid, Shehzad; Razzaq, Shahid: TOBAE: a density-based agglomerative clustering algorithm (2015)
  11. Lipovetsky, Stan: MANOVA, LDA, and FA criteria in clusters parameter estimation (2015)
  12. Meng, Jun; Li, Rui; Luan, Yushi: Classification by integrating plant stress response gene expression data with biological knowledge (2015)
  13. Nellore, Abhinav; Ward, Rachel: Recovery guarantees for exemplar-based clustering (2015)
  14. Panagiotakis, Costas: Point clustering via voting maximization (2015)
  15. Zok, Tomasz; Antczak, Maciej; Riedel, Martin; Nebel, David; Villmann, Thomas; Lukasiak, Piotr; Blazewicz, Jacek; Szachniuk, Marta: Building the library of RNA 3D nucleotide conformations using the clustering approach (2015)
  16. Cagnina, Leticia; Errecalde, Marcelo; Ingaramo, Diego; Rosso, Paolo: An efficient particle swarm optimization approach to cluster short texts (2014) ioport
  17. Kong, W.W.; Ranganath, Surendra: Towards subject independent continuous sign language recognition: a segment and merge approach (2014) ioport
  18. Li, Jia; Tian, Yonghong; Huang, Tiejun: Visual saliency with statistical priors (2014)
  19. Richarz, Jan; Vajda, Szilard; Grzeszick, Rene; Fink, Gernot A.: Semi-supervised learning for character recognition in historical archive documents (2014) ioport
  20. Sanchez, Mauricio A.; Castillo, Oscar; Castro, Juan R.; Melin, Patricia: Fuzzy granular gravitational clustering algorithm for multivariate data (2014)

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