IDEAS home Printed from https://ideas.repec.org/r/spr/advdac/v8y2014i3p231-255.html
   My bibliography  Save this item

Functional data clustering: a survey

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Tapia, Mariela & Heinemann, Detlev & Ballari, Daniela & Zondervan, Edwin, 2022. "Spatio-temporal characterization of long-term solar resource using spatial functional data analysis: Understanding the variability and complementarity of global horizontal irradiance in Ecuador," Renewable Energy, Elsevier, vol. 189(C), pages 1176-1193.
  2. Yifan Zhu & Chongzhi Di & Ying Qing Chen, 2019. "Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 238-261, July.
  3. Vogt, Michael & Linton, Oliver, 2020. "Multiscale clustering of nonparametric regression curves," Journal of Econometrics, Elsevier, vol. 216(1), pages 305-325.
  4. Barroso, Joana Maia Fernandes & Albuquerque-Oliveira, João Lucas & Oliveira-Neto, Francisco Moraes, 2020. "Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks," Journal of Transport Geography, Elsevier, vol. 89(C).
  5. Deb, Soudeep & Karmakar, Sayar, 2023. "A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
  6. Michael Vogt & Oliver Linton, 2015. "Classification of nonparametric regression functions in heterogeneous panels," CeMMAP working papers 06/15, Institute for Fiscal Studies.
  7. Paolo Giordani & Serena Perna & Annamaria Bianchi & Antonio Pizzulli & Salvatore Tripodi & Paolo Maria Matricardi, 2020. "A study of longitudinal mobile health data through fuzzy clustering methods for functional data: The case of allergic rhinoconjunctivitis in childhood," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-23, November.
  8. Mijeong Kim & Shili Lin, 2020. "Characterization of histone modification patterns and prediction of novel promoters using functional principal component analysis," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-16, May.
  9. Snježana Majstorović & Kristian Sabo & Johannes Jung & Matija Klarić, 2018. "Spectral methods for growth curve clustering," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 715-737, September.
  10. Kim, Joonpyo & Oh, Hee-Seok, 2020. "Pseudo-quantile functional data clustering," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
  11. Pedro C. Álvarez-Esteban & Luis A. García-Escudero, 2022. "Robust clustering of functional directional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 181-199, March.
  12. Wang, Bingling & Li, Yingxing & Härdle, Wolfgang Karl, 2022. "K-expectiles clustering," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  13. Ja‐Yoon Jang & Hee‐Seok Oh & Yaeji Lim & Ying Kuen Cheung, 2021. "Ensemble clustering for step data via binning," Biometrics, The International Biometric Society, vol. 77(1), pages 293-304, March.
  14. Qingzhi Zhong & Huazhen Lin & Yi Li, 2021. "Cluster non‐Gaussian functional data," Biometrics, The International Biometric Society, vol. 77(3), pages 852-865, September.
  15. Amovin-Assagba, Martial & Gannaz, Irène & Jacques, Julien, 2022. "Outlier detection in multivariate functional data through a contaminated mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
  16. Petrovich, Justin & Reimherr, Matthew, 2017. "Asymptotic properties of principal component projections with repeated eigenvalues," Statistics & Probability Letters, Elsevier, vol. 130(C), pages 42-48.
  17. C. Denis & E. Lebarbier & C. Lévy‐Leduc & O. Martin & L. Sansonnet, 2020. "A novel regularized approach for functional data clustering: an application to milking kinetics in dairy goats," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 623-640, June.
  18. Fang, Kuangnan & Chen, Yuanxing & Ma, Shuangge & Zhang, Qingzhao, 2022. "Biclustering analysis of functionals via penalized fusion," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  19. S. Barahona & P. Centella & X. Gual-Arnau & M. V. Ibáñez & A. Simó, 2020. "Supervised classification of geometrical objects by integrating currents and functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 637-660, September.
  20. Jonatan A. González & Francisco J. Rodríguez-Cortés & Elvira Romano & Jorge Mateu, 2021. "Classification of Events Using Local Pair Correlation Functions for Spatial Point Patterns," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 538-559, December.
  21. Vieu, Philippe, 2018. "On dimension reduction models for functional data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 134-138.
  22. Deqing Wang & Zhangqi Zhong & Kaixu Bai & Lingyun He, 2019. "Spatial and Temporal Variabilities of PM 2.5 Concentrations in China Using Functional Data Analysis," Sustainability, MDPI, vol. 11(6), pages 1-20, March.
  23. Jacopo Canello & Francesco Vidoli, 2020. "Investigating space‐time patterns of regional industrial resilience through a micro‐level approach: An application to the Italian wine industry," Journal of Regional Science, Wiley Blackwell, vol. 60(4), pages 653-676, September.
  24. Chuyuan Lin & Ying Yu & Lucas Y. Wu & Jiguo Cao, 2023. "Unsupervised learning on U.S. weather forecast performance," Computational Statistics, Springer, vol. 38(3), pages 1193-1213, September.
  25. Javier Albert-Smet & Aurora Torrente & Juan Romo, 2023. "Band depth based initialization of K-means for functional data clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 463-484, June.
  26. O. I. Traore & P. Cristini & N. Favretto-Cristini & L. Pantera & P. Vieu & S. Viguier-Pla, 2019. "Clustering acoustic emission signals by mixing two stages dimension reduction and nonparametric approaches," Computational Statistics, Springer, vol. 34(2), pages 631-652, June.
  27. Christian Esposito & Marco Gortan & Lorenzo Testa & Francesca Chiaromonte & Giorgio Fagiolo & Andrea Mina & Giulio Rossetti, 2021. "Can you always reap what you sow? Network and functional data analysis of VC investments in health-tech companies," Papers 2111.06371, arXiv.org.
  28. Alessandro Casa & Charles Bouveyron & Elena Erosheva & Giovanna Menardi, 2021. "Co-clustering of Time-Dependent Data via the Shape Invariant Model," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 626-649, October.
  29. Michael Vogt & Oliver Linton, 2015. "Classification of nonparametric regression functions in heterogeneous panels," CeMMAP working papers CWP06/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  30. Christian Acal & Ana M. Aguilera & Manuel Escabias, 2020. "New Modeling Approaches Based on Varimax Rotation of Functional Principal Components," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
  31. Carlos Barrera-Causil & Juan Carlos Correa & Andrew Zamecnik & Francisco Torres-Avilés & Fernando Marmolejo-Ramos, 2021. "An FDA-Based Approach for Clustering Elicited Expert Knowledge," Stats, MDPI, vol. 4(1), pages 1-21, March.
  32. Aneiros, Germán & Cao, Ricardo & Fraiman, Ricardo & Genest, Christian & Vieu, Philippe, 2019. "Recent advances in functional data analysis and high-dimensional statistics," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 3-9.
  33. Aneiros, Germán & Horová, Ivana & Hušková, Marie & Vieu, Philippe, 2022. "On functional data analysis and related topics," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  34. Elvira Romano & Jorge Mateu & Ramon Giraldo, 2015. "On the performance of two clustering methods for spatial functional data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(4), pages 467-492, October.
  35. Yao, Binhong & Li, Peixing, 2023. "Covariance estimation error of incomplete functional data under RKHS framework," Applied Mathematics and Computation, Elsevier, vol. 443(C).
  36. Xin Yao & Yuanyuan Cheng & Li Zhou & Malin Song, 2022. "Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods," Annals of Operations Research, Springer, vol. 308(1), pages 727-752, January.
  37. Christian Esposito & Marco Gortan & Lorenzo Testa & Francesca Chiaromonte & Giorgio Fagiolo & Andrea Mina & Giulio Rossetti, 2022. "Venture capital investments through the lens of network and functional data analysis," LEM Papers Series 2022/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  38. Ramón Giraldo & William Caballero & Jesús Camacho-Tamayo, 2018. "Mantel test for spatial functional data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 21-39, January.
  39. Galvani, Marta & Torti, Agostino & Menafoglio, Alessandra & Vantini, Simone, 2021. "FunCC: A new bi-clustering algorithm for functional data with misalignment," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
  40. Ainhoa-Elena Léger & Stefano Mazzuco, 2021. "What Can We Learn from the Functional Clustering of Mortality Data? An Application to the Human Mortality Database," European Journal of Population, Springer;European Association for Population Studies, vol. 37(4), pages 769-798, November.
  41. Golovkine, Steven & Klutchnikoff, Nicolas & Patilea, Valentin, 2022. "Clustering multivariate functional data using unsupervised binary trees," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
  42. Gianluca Sottile & Giada Adelfio, 2019. "Clusters of effects curves in quantile regression models," Computational Statistics, Springer, vol. 34(2), pages 551-569, June.
  43. Benjamin Auder & Jairo Cugliari & Yannig Goude & Jean-Michel Poggi, 2018. "Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting," Energies, MDPI, vol. 11(7), pages 1-22, July.
  44. Francisco Martínez-Álvarez & Amandine Schmutz & Gualberto Asencio-Cortés & Julien Jacques, 2018. "A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand," Energies, MDPI, vol. 12(1), pages 1-18, December.
  45. Leila M Naeni & Hugh Craig & Regina Berretta & Pablo Moscato, 2016. "A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-27, August.
  46. Boudreault, Jeremie & Bergeron, Normand E & St-Hilaire, Andre & Chebana, Fateh, 2022. "A new look at habitat suitability curves through functional data analysis," Ecological Modelling, Elsevier, vol. 467(C).
  47. Christian Acal & Ana M. Aguilera, 2023. "Basis expansion approaches for functional analysis of variance with repeated measures," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 291-321, June.
  48. Michael Vogt & Oliver Linton, 2017. "Classification of non-parametric regression functions in longitudinal data models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 5-27, January.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.