R package funHDDC: Model-based clustering in group-specific functional subspaces: Model-based clustering of time series in group-specific functional subspacesThis work develops a general procedure for clustering functional data which adapts the clustering method high dimensional data clustering (HDDC), originally proposed in the multivariate context. The resulting clustering method, called funHDDC, is based on a functional latent mixture model which fits the functional data in group-specific functional subspaces. By constraining model parameters within and between groups, a family of parsimonious models is exhibited which allow to fit onto various situations. An estimation procedure based on the EM algorithm is proposed for determining both the model parameters and the group-specific functional subspaces. Experiments on real-world datasets show that the proposed approach performs better or similarly than classical two-step clustering methods while providing useful interpretations of the groups and avoiding the uneasy choice of the discretization technique. In particular, funHDDC appears to always outperform HDDC applied on spline coefficients.

References in zbMATH (referenced in 26 articles )

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  1. Golovkine, Steven; Klutchnikoff, Nicolas; Patilea, Valentin: Clustering multivariate functional data using unsupervised binary trees (2022)
  2. Levantesi, Susanna; Nigri, Andrea; Piscopo, Gabriella: Clustering-based simultaneous forecasting of life expectancy time series through Long-Short Term Memory Neural Networks (2022)
  3. Biernacki, Christophe; Marbac, Matthieu; Vandewalle, Vincent: Gaussian-based visualization of Gaussian and non-Gaussian-based clustering (2021)
  4. Casa, Alessandro; Bouveyron, Charles; Erosheva, Elena; Menardi, Giovanna: Co-clustering of time-dependent data via the shape invariant model (2021)
  5. Liang, Decai; Zhang, Haozhe; Chang, Xiaohui; Huang, Hui: Modeling and regionalization of China’s (\mathrmPM_2.5) using spatial-functional mixture models (2021)
  6. Sharp, Alex; Browne, Ryan: Functional data clustering by projection into latent generalized hyperbolic subspaces (2021)
  7. Steven Golovkine: FDApy: a Python package for functional data (2021) arXiv
  8. Barinder Thind, Sidi Wu, Richard Groenewald, Jiguo Cao: FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces (2020) arXiv
  9. Kim, Joonpyo; Oh, Hee-Seok: Pseudo-quantile functional data clustering (2020)
  10. Schmutz, Amandine; Jacques, Julien; Bouveyron, Charles; Chèze, Laurence; Martin, Pauline: Clustering multivariate functional data in group-specific functional subspaces (2020)
  11. Rivera-García, Diego; García-Escudero, Luis A.; Mayo-Iscar, Agustín; Ortega, Joaquín: Robust clustering for functional data based on trimming and constraints (2019)
  12. Zambom, Adriano Zanin; Collazos, Julian A. A.; Dias, Ronaldo: Functional data clustering via hypothesis testing (k)-means (2019)
  13. Zeng, Pengcheng; Shi, Jian Qing; Kim, Won-Seok: Simultaneous registration and clustering for multidimensional functional data (2019)
  14. Justel, Ana; Svarc, Marcela: A divisive clustering method for functional data with special consideration of outliers (2018)
  15. Meng, Yinfeng; Liang, Jiye; Cao, Fuyuan; He, Yijun: A new distance with derivative information for functional (k)-means clustering algorithm (2018)
  16. Clara Happ: Object-Oriented Software for Functional Data (2017) arXiv
  17. Yamamoto, Michio; Hwang, Heungsun: Dimension-reduced clustering of functional data via subspace separation (2017)
  18. Bouveyron, C.; Fauvel, M.; Girard, S.: Kernel discriminant analysis and clustering with parsimonious Gaussian process models (2015)
  19. Bouveyron, Charles; Côme, Etienne; Jacques, Julien: The discriminative functional mixture model for a comparative analysis of bike sharing systems (2015)
  20. Jacques, Julien; Preda, Cristian: Model-based clustering for multivariate functional data (2014)

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