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 104 articles )

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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. 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)
  3. Brusco, Michael J.; Steinley, Douglas; Stevens, Jordan; Cradit, J. Dennis: Affinity propagation: an exemplar-based tool for clustering in psychological research (2019)
  4. Dai, Guowei; Li, Fengwei; Sun, Yuefang; Xu, Dachuan; Zhang, Xiaoyan: Convergence and correctness of belief propagation for the Chinese postman problem (2019)
  5. Deng, Ping; Wang, Hongjun; Li, Tianrui; Horng, Shi-Jinn; Zhu, Xinwen: Linear discriminant analysis guided by unsupervised ensemble learning (2019)
  6. Hennig, Christian; Viroli, Cinzia; Anderlucci, Laura: Quantile-based clustering (2019)
  7. Liu, Cong; Chen, Qianqian; Chen, Yingxia; Liu, Jie: A fast multiobjective fuzzy clustering with multimeasures combination (2019)
  8. Long, Andrew W.; Ferguson, Andrew L.: Landmark diffusion maps (L-dMaps): accelerated manifold learning out-of-sample extension (2019)
  9. Wang, Hongjun; Zhang, Yinghui; Zhang, Ji; Li, Tianrui; Peng, Lingxi: A factor graph model for unsupervised feature selection (2019)
  10. Bottarelli, Lorenzo; Bicego, Manuele; Denitto, Matteo; Di Pierro, Alessandra; Farinelli, Alessandro; Mengoni, Riccardo: Biclustering with a quantum annealer (2018)
  11. Brodinová, Šárka; Zaharieva, Maia; Filzmoser, Peter; Ortner, Thomas; Breiteneder, Christian: Clustering of imbalanced high-dimensional media data (2018)
  12. Gu, Xiaowei; Angelov, Plamen; Kangin, Dmitry; Principe, Jose: Self-organised direction aware data partitioning algorithm (2018)
  13. 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)
  14. Zhang, Shu; Li, Lijuan; Yao, Lijuan; Yang, Shipin; Zou, Tao: Data-driven process decomposition and robust online distributed modelling for large-scale processes (2018)
  15. Zhu, Hong; He, Hanzhi; Xu, Jinhui; Fang, Qianhao; Wang, Wei: Medical image segmentation using fruit fly optimization and density peaks clustering (2018)
  16. Denitto, M.; Farinelli, A.; Figueiredo, M. A. T.; Bicego, M.: A biclustering approach based on factor graphs and the max-sum algorithm (2017)
  17. Huang, Jinlong; Zhu, Qingsheng; Yang, Lijun; Cheng, Dongdong; Wu, Quanwang: QCC: a novel clustering algorithm based on quasi-cluster centers (2017)
  18. Hu, Chenyue W.; Li, Hanyang; Qutub, Amina A.: Shrinkage clustering: a fast and size-constrained algorithm for biomedical applications (2017)
  19. Wang, Lan; Cheng, Yu; Hu, Jinglu; Liang, Jinling; Dobaie, Abdullah M.: Nonlinear system identification using quasi-ARX RBFN models with a parameter-classified scheme (2017)
  20. Deng, Zhi-Hong; Xu, Xiaoran: A novel probabilistic clustering model for heterogeneous networks (2016)

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