Clusterpath: An Algorithm for Clustering using Convex Fusion Penalties. We present a new clustering algorithm by proposing a convex relaxation of hierarchical clustering, which results in a family of objective functions with a natural geometric interpretation. We give efficient algorithms for calculating the continuous regularization path of solutions, and discuss relative advantages of the parameters. Our method experimentally gives state-of-the-art results similar to spectral clustering for non-convex clusters, and has the added benefit of learning a tree structure from the data.

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  1. Chen, Jingxiang; Tran-Dinh, Quoc; Kosorok, Michael R.; Liu, Yufeng: Identifying heterogeneous effect using latent supervised clustering with adaptive fusion (2021)
  2. Degras, David: Sparse group fused Lasso for model segmentation: a hybrid approach (2021)
  3. Lu, Wenqi; Qin, Guoyou; Zhu, Zhongyi; Tu, Dongsheng: Multiply robust subgroup identification for longitudinal data with dropouts via median regression (2021)
  4. Pi, J.; Wang, Honggang; Pardalos, Panos M.: A dual reformulation and solution framework for regularized convex clustering problems (2021)
  5. Sun, Defeng; Toh, Kim-Chuan; Yuan, Yancheng: Convex clustering: model, theoretical guarantee and efficient algorithm (2021)
  6. Wang, Minjie; Allen, Genevera I.: Integrative generalized convex clustering optimization and feature selection for mixed multi-view data (2021)
  7. Zhang, Tonglin; Lin, Ge: Generalized (k)-means in GLMs with applications to the outbreak of COVID-19 in the United States (2021)
  8. Chen, Huangyue; Kong, Lingchen; Li, Yan: A novel convex clustering method for high-dimensional data using semiproximal ADMM (2020)
  9. Chi, Eric C.; Gaines, Brian J.; Sun, Will Wei; Zhou, Hua; Yang, Jian: Provable convex co-clustering of tensors (2020)
  10. Jiang, Tao; Vavasis, Stephen; Zhai, Chen Wen: Recovery of a mixture of Gaussians by sum-of-norms clustering (2020)
  11. Li, Tianxi; Qian, Cheng; Levina, Elizaveta; Zhu, Ji: High-dimensional Gaussian graphical models on network-linked data (2020)
  12. Ng, Chi Tim; Lee, Woojoo; Lee, Youngjo: Logical and test consistency in pairwise multiple comparisons (2020)
  13. Panahi, Ashkan; Chehreghani, Morteza Haghir; Dubhashi, Devdatt: Accelerated proximal incremental algorithm schemes for non-strongly convex functions (2020)
  14. Saha, Sujayam; Guntuboyina, Adityanand: On the nonparametric maximum likelihood estimator for Gaussian location mixture densities with application to Gaussian denoising (2020)
  15. Weylandt, Michael; Nagorski, John; Allen, Genevera I.: Dynamic visualization and fast computation for convex clustering via algorithmic regularization (2020)
  16. Yang, Xinfeng; Yan, Xiaodong: Mechanism and a new algorithm for nonconvex clustering (2020)
  17. Zheng, Peng; Aravkin, Aleksandr: Relax-and-split method for nonconvex inverse problems (2020)
  18. Chi, Eric C.; Steinerberger, Stefan: Recovering trees with convex clustering (2019)
  19. Gu, Jiaying; Volgushev, Stanislav: Panel data quantile regression with grouped fixed effects (2019)
  20. Price, Bradley S.; Geyer, Charles J.; Rothman, Adam J.: Automatic response category combination in multinomial logistic regression (2019)

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