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 116 articles )

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  1. Anderlucci, Laura; Fortunato, Francesca; Montanari, Angela: High-dimensional clustering via random projections (2022)
  2. Zheng, Yuchen; Xie, Yujia; Lee, Ilbin; Dehghanian, Amin; Serban, Nicoleta: Parallel subgradient algorithm with block dual decomposition for large-scale optimization (2022)
  3. Coelho, André L. V.; Sandes, Nelson C.: Data clustering via cooperative games: a novel approach and comparative study (2021)
  4. Ibrahim, Mohamed Hamza; Missaoui, Rokia: An exemplar-based clustering using efficient variational message passing (2021)
  5. Michael C. Thrun, Quirin Stier: Fundamental clustering algorithms suite (2021) not zbMATH
  6. Thrun, Michael C.; Ultsch, Alfred: Using projection-based clustering to find distance- and density-based clusters in high-dimensional data (2021)
  7. Ushakov, Anton V.; Vasilyev, Igor: Near-optimal large-scale k-medoids clustering (2021)
  8. Wu, Yanping; Zhang, Yinghui; Wang, Hongjun; Deng, Ping; Li, Tianrui: Enhanced clustering embedded in curvilinear distance analysis guided by pairwise constraints (2021)
  9. Chunaev, Petr: Community detection in node-attributed social networks: a survey (2020)
  10. Li, Hailin; Wu, Yenchun Jim; Chen, Yewang: Time is money: dynamic-model-based time series data-mining for correlation analysis of commodity sales (2020)
  11. Li, Min; Xu, Dachuan; Yue, Jun; Zhang, Dongmei: The parallel seeding algorithm for (k)-means problem with penalties (2020)
  12. 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)
  13. Brusco, Michael J.; Steinley, Douglas; Stevens, Jordan; Cradit, J. Dennis: Affinity propagation: an exemplar-based tool for clustering in psychological research (2019)
  14. Comas-Cufí, Marc; Martín-Fernández, Josep A.; Mateu-Figueras, Glòria: Merging the components of a finite mixture using posterior probabilities (2019)
  15. Dai, Guowei; Li, Fengwei; Sun, Yuefang; Xu, Dachuan; Zhang, Xiaoyan: Convergence and correctness of belief propagation for the Chinese postman problem (2019)
  16. Deng, Ping; Wang, Hongjun; Li, Tianrui; Horng, Shi-Jinn; Zhu, Xinwen: Linear discriminant analysis guided by unsupervised ensemble learning (2019)
  17. Hennig, Christian; Viroli, Cinzia; Anderlucci, Laura: Quantile-based clustering (2019)
  18. Liu, Cong; Chen, Qianqian; Chen, Yingxia; Liu, Jie: A fast multiobjective fuzzy clustering with multimeasures combination (2019)
  19. Long, Andrew W.; Ferguson, Andrew L.: Landmark diffusion maps (L-dMaps): accelerated manifold learning out-of-sample extension (2019)
  20. Wang, Hongjun; Zhang, Yinghui; Zhang, Ji; Li, Tianrui; Peng, Lingxi: A factor graph model for unsupervised feature selection (2019)

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