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 108 articles )

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  1. Michael C. Thrun, Quirin Stier: Fundamental clustering algorithms suite (2021) not zbMATH
  2. Chunaev, Petr: Community detection in node-attributed social networks: a survey (2020)
  3. Li, Hailin; Wu, Yenchun Jim; Chen, Yewang: Time is money: dynamic-model-based time series data-mining for correlation analysis of commodity sales (2020)
  4. Li, Min; Xu, Dachuan; Yue, Jun; Zhang, Dongmei: The parallel seeding algorithm for (k)-means problem with penalties (2020)
  5. 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)
  6. Brusco, Michael J.; Steinley, Douglas; Stevens, Jordan; Cradit, J. Dennis: Affinity propagation: an exemplar-based tool for clustering in psychological research (2019)
  7. Comas-Cufí, Marc; Martín-Fernández, Josep A.; Mateu-Figueras, Glòria: Merging the components of a finite mixture using posterior probabilities (2019)
  8. Dai, Guowei; Li, Fengwei; Sun, Yuefang; Xu, Dachuan; Zhang, Xiaoyan: Convergence and correctness of belief propagation for the Chinese postman problem (2019)
  9. Deng, Ping; Wang, Hongjun; Li, Tianrui; Horng, Shi-Jinn; Zhu, Xinwen: Linear discriminant analysis guided by unsupervised ensemble learning (2019)
  10. Hennig, Christian; Viroli, Cinzia; Anderlucci, Laura: Quantile-based clustering (2019)
  11. Liu, Cong; Chen, Qianqian; Chen, Yingxia; Liu, Jie: A fast multiobjective fuzzy clustering with multimeasures combination (2019)
  12. Long, Andrew W.; Ferguson, Andrew L.: Landmark diffusion maps (L-dMaps): accelerated manifold learning out-of-sample extension (2019)
  13. Wang, Hongjun; Zhang, Yinghui; Zhang, Ji; Li, Tianrui; Peng, Lingxi: A factor graph model for unsupervised feature selection (2019)
  14. Bottarelli, Lorenzo; Bicego, Manuele; Denitto, Matteo; Di Pierro, Alessandra; Farinelli, Alessandro; Mengoni, Riccardo: Biclustering with a quantum annealer (2018)
  15. Brodinová, Šárka; Zaharieva, Maia; Filzmoser, Peter; Ortner, Thomas; Breiteneder, Christian: Clustering of imbalanced high-dimensional media data (2018)
  16. Gu, Xiaowei; Angelov, Plamen; Kangin, Dmitry; Principe, Jose: Self-organised direction aware data partitioning algorithm (2018)
  17. 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)
  18. Ma, Shugao; Zhang, Jianming; Sclaroff, Stan; Ikizler-Cinbis, Nazli; Sigal, Leonid: Space-time tree ensemble for action recognition and localization (2018)
  19. Zhang, Shu; Li, Lijuan; Yao, Lijuan; Yang, Shipin; Zou, Tao: Data-driven process decomposition and robust online distributed modelling for large-scale processes (2018)
  20. Zhu, Hong; He, Hanzhi; Xu, Jinhui; Fang, Qianhao; Wang, Wei: Medical image segmentation using fruit fly optimization and density peaks clustering (2018)

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