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Variable Selection for Model-Based Clustering

Citations

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Cited by:

  1. Gilles Celeux & Cathy Maugis-Rabusseau & Mohammed Sedki, 2019. "Variable selection in model-based clustering and discriminant analysis with a regularization approach," 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. 13(1), pages 259-278, March.
  2. Csereklyei, Zsuzsanna & Anantharama, Nandini & Kallies, Anne, 2021. "Electricity market transitions in Australia: Evidence using model-based clustering," Energy Economics, Elsevier, vol. 103(C).
  3. Stefano Tonellato, 2017. "From Dirichlet Process mixture models to spectral clustering," Working Papers 2017:33, Department of Economics, University of Venice "Ca' Foscari".
  4. repec:jss:jstsof:18:i06 is not listed on IDEAS
  5. Scrucca, Luca, 2016. "Identifying connected components in Gaussian finite mixture models for clustering," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 5-17.
  6. Léna Carel & Pierre Alquier, 2021. "Simultaneous dimension reduction and clustering via the NMF-EM algorithm," 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. 15(1), pages 231-260, March.
  7. Mantas Svazas & Valentinas Navickas & Yuriy Bilan & Joanna Nakonieczny & Jana Spankova, 2021. "Biomass Clusterization from a Regional Perspective: The Case of Lithuania," Energies, MDPI, vol. 14(21), pages 1-15, October.
  8. Sunil Kumar & Apurba Vishal Dabgotra, 2021. "A latent class analysis on the usage of mobile phones among management students," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 89-114, March.
  9. Šárka Brodinová & Peter Filzmoser & Thomas Ortner & Christian Breiteneder & Maia Rohm, 2019. "Robust and sparse k-means clustering for high-dimensional 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. 13(4), pages 905-932, December.
  10. Crespo Cuaresma, Jesus & Doppelhofer, Gernot, 2007. "Nonlinearities in cross-country growth regressions: A Bayesian Averaging of Thresholds (BAT) approach," Journal of Macroeconomics, Elsevier, vol. 29(3), pages 541-554, September.
  11. Nema Dean & Adrian Raftery, 2010. "Latent class analysis variable selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 11-35, February.
  12. Jerzy Korzeniewski, 2016. "New Method Of Variable Selection For Binary Data Cluster Analysis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(2), pages 295-304, June.
  13. Mai, Feng & Fry, Michael J. & Ohlmann, Jeffrey W., 2018. "Model-based capacitated clustering with posterior regularization," European Journal of Operational Research, Elsevier, vol. 271(2), pages 594-605.
  14. Stefano Tonellato, 2019. "Bayesian nonparametric clustering as a community detection problem," Working Papers 2019: 20, Department of Economics, University of Venice "Ca' Foscari".
  15. Melnykov, Volodymyr, 2016. "Model-based biclustering of clickstream data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 31-45.
  16. Na Li & Steven Jige Quan, 2023. "Identifying urban form typologies in Seoul using a new Gaussian mixture model-based clustering framework," Environment and Planning B, , vol. 50(9), pages 2342-2358, November.
  17. Paul D. McNicholas, 2016. "Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 331-373, October.
  18. Montanari, Angela & Viroli, Cinzia, 2011. "Maximum likelihood estimation of mixtures of factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2712-2723, September.
  19. Fulvia Pennoni & Francesco Bartolucci & Silvia Pandolfi, 2024. "Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data," Journal of Classification, Springer;The Classification Society, vol. 41(3), pages 568-589, November.
  20. Benhuai Xie & Wei Pan & Xiaotong Shen, 2008. "Variable Selection in Penalized Model‐Based Clustering Via Regularization on Grouped Parameters," Biometrics, The International Biometric Society, vol. 64(3), pages 921-930, September.
  21. Monia Ranalli & Roberto Rocci, 2017. "A Model-Based Approach to Simultaneous Clustering and Dimensional Reduction of Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1007-1034, December.
  22. Dutta Rishav & Yurko Ronald & Ventura Samuel L., 2020. "Unsupervised methods for identifying pass coverage among defensive backs with NFL player tracking data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(2), pages 143-161, June.
  23. Chen, Jiahua & Tan, Xianming, 2009. "Inference for multivariate normal mixtures," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1367-1383, August.
  24. Cappozzo, Andrea & Greselin, Francesca & Murphy, Thomas Brendan, 2021. "Robust variable selection for model-based learning in presence of adulteration," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
  25. Jerzy Korzeniewski, 2016. "New Method Of Variable Selection For Binary Data Cluster Analysis," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 295-304, June.
  26. Dolnicar, Sara & Grün, Bettina & Leisch, Friedrich, 2016. "Increasing sample size compensates for data problems in segmentation studies," Journal of Business Research, Elsevier, vol. 69(2), pages 992-999.
  27. Germán Caruso & Walter Sosa-Escudero & Marcela Svarc, 2015. "Deprivation and the Dimensionality of Welfare: A Variable-Selection Cluster-Analysis Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(4), pages 702-722, December.
  28. Michael Fop & Pierre-Alexandre Mattei & Charles Bouveyron & Thomas Brendan Murphy, 2022. "Unobserved classes and extra variables in high-dimensional discriminant analysis," 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 55-92, March.
  29. Hivert, Benjamin & Agniel, Denis & Thiébaut, Rodolphe & Hejblum, Boris P., 2024. "Post-clustering difference testing: Valid inference and practical considerations with applications to ecological and biological data," Computational Statistics & Data Analysis, Elsevier, vol. 193(C).
  30. Arias-Castro, Ery & Pu, Xiao, 2017. "A simple approach to sparse clustering," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 217-228.
  31. Tsai, Chieh-Yuan & Chiu, Chuang-Cheng, 2008. "Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4658-4672, June.
  32. Zhang, Q. & Ip, E.H., 2014. "Variable assessment in latent class models," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 146-156.
  33. Bouveyron, C. & Girard, S. & Schmid, C., 2007. "High-dimensional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 502-519, September.
  34. Andrews, Jeffrey L. & McNicholas, Paul D. & Subedi, Sanjeena, 2011. "Model-based classification via mixtures of multivariate t-distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 520-529, January.
  35. Gaynor, Sheila & Bair, Eric, 2017. "Identification of relevant subtypes via preweighted sparse clustering," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 139-154.
  36. Rendon Aguirre, Janeth Carolina, 2017. "Clustering Big Data by Extreme Kurtosis Projections," DES - Working Papers. Statistics and Econometrics. WS 24522, Universidad Carlos III de Madrid. Departamento de Estadística.
  37. Christophe Biernacki & Alexandre Lourme, 2019. "Unifying data units and models in (co-)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. 13(1), pages 7-31, March.
  38. Maugis, C. & Celeux, G. & Martin-Magniette, M.-L., 2009. "Variable selection in model-based clustering: A general variable role modeling," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3872-3882, September.
  39. Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.
  40. Léna CAREL & Pierre ALQUIER, 2017. "Simultaneous Dimension Reduction and Clustering via the NMF-EM Algorithm," Working Papers 2017-38, Center for Research in Economics and Statistics.
  41. Zhaoyu Xing & Yang Wan & Juan Wen & Wei Zhong, 2024. "GOLFS: feature selection via combining both global and local information for high dimensional clustering," Computational Statistics, Springer, vol. 39(5), pages 2651-2675, July.
  42. Marbac, Matthieu & Vandewalle, Vincent, 2019. "A tractable multi-partitions clustering," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 167-179.
  43. Dunstan, Piers K. & Foster, Scott D. & Darnell, Ross, 2011. "Model based grouping of species across environmental gradients," Ecological Modelling, Elsevier, vol. 222(4), pages 955-963.
  44. Susan Brudvig & Michael J. Brusco & J. Dennis Cradit, 2019. "Joint selection of variables and clusters: recovering the underlying structure of marketing data," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(1), pages 1-12, March.
  45. Douglas Steinley & Michael Brusco, 2008. "Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures," Psychometrika, Springer;The Psychometric Society, vol. 73(1), pages 125-144, March.
  46. Jian Guo & Elizaveta Levina & George Michailidis & Ji Zhu, 2010. "Pairwise Variable Selection for High-Dimensional Model-Based Clustering," Biometrics, The International Biometric Society, vol. 66(3), pages 793-804, September.
  47. Charles Bouveyron & Camille Brunet-Saumard, 2014. "Discriminative variable selection for clustering with the sparse Fisher-EM algorithm," Computational Statistics, Springer, vol. 29(3), pages 489-513, June.
  48. Bouveyron, Charles & Brunet-Saumard, Camille, 2014. "Model-based clustering of high-dimensional data: A review," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 52-78.
  49. Cozzini, Alberto & Jasra, Ajay & Montana, Giovanni & Persing, Adam, 2014. "A Bayesian mixture of lasso regressions with t-errors," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 84-97.
  50. Crook Oliver M. & Gatto Laurent & Kirk Paul D. W., 2019. "Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(6), pages 1-20, December.
  51. Hunt, Lynette A. & Basford, Kaye E., 2016. "Comparing classical criteria for selecting intra-class correlated features in Multimix," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 350-366.
  52. Sahin, Özge & Czado, Claudia, 2022. "Vine copula mixture models and clustering for non-Gaussian data," Econometrics and Statistics, Elsevier, vol. 22(C), pages 136-158.
  53. Maugis, C. & Celeux, G. & Martin-Magniette, M.-L., 2011. "Variable selection in model-based discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 102(10), pages 1374-1387, November.
  54. Galimberti, Giuliano & Montanari, Angela & Viroli, Cinzia, 2009. "Penalized factor mixture analysis for variable selection in clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4301-4310, October.
  55. Csereklyei, Zsuzsanna & Thurner, Paul W. & Langer, Johannes & Küchenhoff, Helmut, 2017. "Energy paths in the European Union: A model-based clustering approach," Energy Economics, Elsevier, vol. 65(C), pages 442-457.
  56. Luca Scrucca & Adrian Raftery, 2015. "Improved initialisation of model-based clustering using Gaussian hierarchical partitions," 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. 9(4), pages 447-460, December.
  57. Ahlquist, John S. & Breunig, Christian, 2009. "Country clustering in comparative political economy," MPIfG Discussion Paper 09/5, Max Planck Institute for the Study of Societies.
  58. Nema Dean & Rebecca Nugent, 2013. "Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas," 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. 7(3), pages 339-357, September.
  59. Cathy Maugis & Gilles Celeux & Marie-Laure Martin-Magniette, 2009. "Variable Selection for Clustering with Gaussian Mixture Models," Biometrics, The International Biometric Society, vol. 65(3), pages 701-709, September.
  60. Sijian Wang & Ji Zhu, 2008. "Variable Selection for Model-Based High-Dimensional Clustering and Its Application to Microarray Data," Biometrics, The International Biometric Society, vol. 64(2), pages 440-448, June.
  61. McNicholas, P.D. & Murphy, T.B. & McDaid, A.F. & Frost, D., 2010. "Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 711-723, March.
  62. Floriello, Davide & Vitelli, Valeria, 2017. "Sparse clustering of functional data," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 1-18.
  63. Golosnoy, Vasyl & Okhrin, Yarema, 2009. "Flexible shrinkage in portfolio selection," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 317-328, February.
  64. Thierry Chekouo & Alejandro Murua, 2018. "High-dimensional variable selection with the plaid mixture model for clustering," Computational Statistics, Springer, vol. 33(3), pages 1475-1496, September.
  65. Fengqin Tang & Chunning Wang & Jinxia Su & Yuanyuan Wang, 2020. "Spectral clustering-based community detection using graph distance and node attributes," Computational Statistics, Springer, vol. 35(1), pages 69-94, March.
  66. Morris, Katherine & McNicholas, Paul D., 2016. "Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 133-150.
  67. Jeffrey Andrews & Paul McNicholas, 2014. "Variable Selection for Clustering and Classification," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 136-153, July.
  68. Daniel Fernández & Richard Arnold & Shirley Pledger & Ivy Liu & Roy Costilla, 2019. "Finite mixture biclustering of discrete type multivariate 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. 13(1), pages 117-143, March.
  69. Alessandro Casa & Andrea Cappozzo & Michael Fop, 2022. "Group-Wise Shrinkage Estimation in Penalized Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 648-674, November.
  70. Morris, Katherine & McNicholas, Paul D., 2013. "Dimension reduction for model-based clustering via mixtures of shifted asymmetric Laplace distributions," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 2088-2093.
  71. Jan Vávra & Arnošt Komárek, 2023. "Classification based on multivariate mixed type longitudinal data with an application to the EU-SILC database," 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 369-406, June.
  72. Katherine Morris & Paul McNicholas & Luca Scrucca, 2013. "Dimension reduction for model-based clustering via mixtures of multivariate $$t$$ t -distributions," 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. 7(3), pages 321-338, September.
  73. Tonellato, Stefano F., 2020. "Bayesian nonparametric clustering as a community detection problem," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
  74. Pełka Marcin, 2019. "Analysis of Happiness in EU Countries Using the Multi-Model Classification based on Models of Symbolic Data," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(3), pages 15-25, September.
  75. Kumar Sunil & Dabgotra Apurba Vishal, 2021. "A latent class analysis on the usage of mobile phones among management students," Statistics in Transition New Series, Statistics Poland, vol. 22(1), pages 89-114, March.
  76. Álvarez, Adolfo, 2009. "Recombining dependent data: an Order Statistics," DES - Working Papers. Statistics and Econometrics. WS ws098526, Universidad Carlos III de Madrid. Departamento de Estadística.
  77. Korzeniewski Jerzy, 2016. "New Method of Variable Selection for Binary Data Cluster Analysis," Statistics in Transition New Series, Statistics Poland, vol. 17(2), pages 295-304, June.
  78. Matthieu Marbac & Mohammed Sedki & Tienne Patin, 2020. "Variable Selection for Mixed Data Clustering: Application in Human Population Genomics," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 124-142, April.
  79. Wang, Ketong & Porter, Michael D., 2018. "Optimal Bayesian clustering using non-negative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 395-411.
  80. Abby Flynt & Nema Dean, 2019. "Growth Mixture Modeling with Measurement Selection," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 3-25, April.
  81. Kemmawadee Preedalikit & Daniel Fernández & Ivy Liu & Louise McMillan & Marta Nai Ruscone & Roy Costilla, 2024. "Row mixture-based clustering with covariates for ordinal responses," Computational Statistics, Springer, vol. 39(5), pages 2511-2555, July.
  82. Gutiérrez, Luis & Gutiérrez-Peña, Eduardo & Mena, Ramsés H., 2014. "Bayesian nonparametric classification for spectroscopy data," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 56-68.
  83. Crespo Cuaresma, Jesus & Doppelhofer, Gernot, 2007. "Nonlinearities in cross-country growth regressions: A Bayesian Averaging of Thresholds (BAT) approach," Journal of Macroeconomics, Elsevier, vol. 29(3), pages 541-554, September.
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