APCluster

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 106 articles , 1 standard article )

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  1. Li, Hailin; Wu, Yenchun Jim; Chen, Yewang: Time is money: dynamic-model-based time series data-mining for correlation analysis of commodity sales (2020)
  2. Li, Min; Xu, Dachuan; Yue, Jun; Zhang, Dongmei: The parallel seeding algorithm for (k)-means problem with penalties (2020)
  3. Boiarov, A. A.; Granichin, O. N.: Stochastic approximation algorithm with randomization at the input for unsupervised parameters estimation of Gaussian mixture model with sparse parameters (2019)
  4. Brusco, Michael J.; Steinley, Douglas; Stevens, Jordan; Cradit, J. Dennis: Affinity propagation: an exemplar-based tool for clustering in psychological research (2019)
  5. Comas-Cufí, Marc; Martín-Fernández, Josep A.; Mateu-Figueras, Glòria: Merging the components of a finite mixture using posterior probabilities (2019)
  6. Dai, Guowei; Li, Fengwei; Sun, Yuefang; Xu, Dachuan; Zhang, Xiaoyan: Convergence and correctness of belief propagation for the Chinese postman problem (2019)
  7. Deng, Ping; Wang, Hongjun; Li, Tianrui; Horng, Shi-Jinn; Zhu, Xinwen: Linear discriminant analysis guided by unsupervised ensemble learning (2019)
  8. Hennig, Christian; Viroli, Cinzia; Anderlucci, Laura: Quantile-based clustering (2019)
  9. Liu, Cong; Chen, Qianqian; Chen, Yingxia; Liu, Jie: A fast multiobjective fuzzy clustering with multimeasures combination (2019)
  10. Long, Andrew W.; Ferguson, Andrew L.: Landmark diffusion maps (L-dMaps): accelerated manifold learning out-of-sample extension (2019)
  11. Wang, Hongjun; Zhang, Yinghui; Zhang, Ji; Li, Tianrui; Peng, Lingxi: A factor graph model for unsupervised feature selection (2019)
  12. Bottarelli, Lorenzo; Bicego, Manuele; Denitto, Matteo; Di Pierro, Alessandra; Farinelli, Alessandro; Mengoni, Riccardo: Biclustering with a quantum annealer (2018)
  13. Brodinová, Šárka; Zaharieva, Maia; Filzmoser, Peter; Ortner, Thomas; Breiteneder, Christian: Clustering of imbalanced high-dimensional media data (2018)
  14. Gu, Xiaowei; Angelov, Plamen; Kangin, Dmitry; Principe, Jose: Self-organised direction aware data partitioning algorithm (2018)
  15. Liu, Wei; Ma, Liangyu; Jeon, Byeungwoo; Chen, Ling; Chen, Bolun: A network hierarchy-based method for functional module detection in protein-protein interaction networks (2018)
  16. Zhang, Shu; Li, Lijuan; Yao, Lijuan; Yang, Shipin; Zou, Tao: Data-driven process decomposition and robust online distributed modelling for large-scale processes (2018)
  17. Zhu, Hong; He, Hanzhi; Xu, Jinhui; Fang, Qianhao; Wang, Wei: Medical image segmentation using fruit fly optimization and density peaks clustering (2018)
  18. Denitto, M.; Farinelli, A.; Figueiredo, M. A. T.; Bicego, M.: A biclustering approach based on factor graphs and the max-sum algorithm (2017)
  19. Huang, Jinlong; Zhu, Qingsheng; Yang, Lijun; Cheng, Dongdong; Wu, Quanwang: QCC: a novel clustering algorithm based on quasi-cluster centers (2017)
  20. Hu, Chenyue W.; Li, Hanyang; Qutub, Amina A.: Shrinkage clustering: a fast and size-constrained algorithm for biomedical applications (2017)

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