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A Framework for Feature Selection in Clustering

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  1. Corinna Kleinert & Alexander Vosseler & Uwe Blien, 2018. "Classifying vocational training markets," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(1), pages 31-48, July.
  2. Peter Radchenko & Gourab Mukherjee, 2017. "Convex clustering via l 1 fusion penalization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1527-1546, November.
  3. Lingsong Meng & Dorina Avram & George Tseng & Zhiguang Huo, 2022. "Outcome‐guided sparse K‐means for disease subtype discovery via integrating phenotypic data with high‐dimensional transcriptomic data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 352-375, March.
  4. Labbé, Martine & Landete, Mercedes & Leal, Marina, 2023. "Dendrograms, minimum spanning trees and feature selection," European Journal of Operational Research, Elsevier, vol. 308(2), pages 555-567.
  5. 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.
  6. Pi, J. & Wang, Honggang & Pardalos, Panos M., 2021. "A dual reformulation and solution framework for regularized convex clustering problems," European Journal of Operational Research, Elsevier, vol. 290(3), pages 844-856.
  7. 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.
  8. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
  9. Emily, Mathieu & Hitte, Christophe & Mom, Alain, 2016. "SMILE: A novel dissimilarity-based procedure for detecting sparse-specific profiles in sparse contingency tables," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 171-188.
  10. Ronglai Shen & Qianxing Mo & Nikolaus Schultz & Venkatraman E Seshan & Adam B Olshen & Jason Huse & Marc Ladanyi & Chris Sander, 2012. "Integrative Subtype Discovery in Glioblastoma Using iCluster," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-9, April.
  11. Dominick Bartelme & Ting Ting & Andrei A. Levchenko, 2020. "Specialization, Market Access and Real Income," Working Papers 679, Research Seminar in International Economics, University of Michigan.
  12. Arias-Castro, Ery & Pu, Xiao, 2017. "A simple approach to sparse clustering," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 217-228.
  13. Gaynor, Sheila & Bair, Eric, 2017. "Identification of relevant subtypes via preweighted sparse clustering," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 139-154.
  14. Peña, Daniel & Prieto Fernández, Francisco Javier & 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.
  15. Chakraborty, Saptarshi & Paul, Debolina & Das, Swagatam, 2020. "Hierarchical clustering with optimal transport," Statistics & Probability Letters, Elsevier, vol. 163(C).
  16. Hosik Choi & Seokho Lee, 2019. "Convex clustering for binary 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 991-1018, December.
  17. Mauricio Toledo-Acosta & Talin Barreiro & Asela Reig-Alamillo & Markus Müller & Fuensanta Aroca Bisquert & Maria Luisa Barrigon & Enrique Baca-Garcia & Jorge Hermosillo-Valadez, 2020. "Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
  18. 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.
  19. Yaeji Lim & Hee-Seok Oh & Ying Kuen Cheung, 2019. "Multiscale Clustering for Functional Data," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 368-391, July.
  20. Yujia Li & Xiangrui Zeng & Chien‐Wei Lin & George C. Tseng, 2022. "Simultaneous estimation of cluster number and feature sparsity in high‐dimensional cluster analysis," Biometrics, The International Biometric Society, vol. 78(2), pages 574-585, June.
  21. Marbac, Matthieu & Vandewalle, Vincent, 2019. "A tractable multi-partitions clustering," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 167-179.
  22. 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.
  23. Maarten M. Kampert & Jacqueline J. Meulman & Jerome H. Friedman, 2017. "rCOSA: A Software Package for Clustering Objects on Subsets of Attributes," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 514-547, October.
  24. 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.
  25. 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.
  26. Banerjee, Trambak & Mukherjee, Gourab & Radchenko, Peter, 2017. "Feature screening in large scale cluster analysis," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 191-212.
  27. Floriello, Davide & Vitelli, Valeria, 2017. "Sparse clustering of functional data," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 1-18.
  28. Clémençon, Stéphan, 2014. "A statistical view of clustering performance through the theory of U-processes," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 42-56.
  29. Zengchao Xu & Shan Luo & Zehua Chen, 2023. "A Portmanteau Local Feature Discrimination Approach to the Classification with High-dimensional Matrix-variate Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 441-467, February.
  30. 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.
  31. Fang, Yixin & Wang, Junhui, 2012. "Selection of the number of clusters via the bootstrap method," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 468-477.
  32. Nan Lin & Junhai Jiang & Shicheng Guo & Momiao Xiong, 2015. "Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-17, July.
  33. Dong Liu & Changwei Zhao & Yong He & Lei Liu & Ying Guo & Xinsheng Zhang, 2023. "Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix‐variate fMRI data," Biometrics, The International Biometric Society, vol. 79(3), pages 2246-2259, September.
  34. 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.
  35. Lucas A Gillenwater & Shahab Helmi & Evan Stene & Katherine A Pratte & Yonghua Zhuang & Ronald P Schuyler & Leslie Lange & Peter J Castaldi & Craig P Hersh & Farnoush Banaei-Kashani & Russell P Bowler, 2021. "Multi-omics subtyping pipeline for chronic obstructive pulmonary disease," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-20, August.
  36. Calin-Adrian Comes & Elena Bunduchi & Valentina Vasile & Daniel Stefan, 2018. "The Impact of Foreign Direct Investments and Remittances on Economic Growth: A Case Study in Central and Eastern Europe," Sustainability, MDPI, vol. 10(1), pages 1-16, January.
  37. Abhishek Bhola & Shailendra Singh, 2019. "Visualisation and Modelling of High-Dimensional Cancerous Gene Expression Dataset," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-22, March.
  38. Fraiman, Ricardo & Gimenez, Yanina & Svarc, Marcela, 2016. "Feature selection for functional data," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 191-208.
  39. Kai Deng & Xin Zhang, 2022. "Tensor envelope mixture model for simultaneous clustering and multiway dimension reduction," Biometrics, The International Biometric Society, vol. 78(3), pages 1067-1079, September.
  40. 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.
  41. Zhiguang Huo & Li Zhu & Tianzhou Ma & Hongcheng Liu & Song Han & Daiqing Liao & Jinying Zhao & George Tseng, 2020. "Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(1), pages 1-22, April.
  42. Huaihou Chen & Philip T. Reiss & Thaddeus Tarpey, 2014. "Optimally weighted L-super-2 distance for functional data," Biometrics, The International Biometric Society, vol. 70(3), pages 516-525, September.
  43. Axel Theorell & Yenan Troi Bryceson & Jakob Theorell, 2019. "Determination of essential phenotypic elements of clusters in high-dimensional entities—DEPECHE," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-15, March.
  44. Baolin Wu, 2013. "Sparse cluster analysis of large-scale discrete variables with application to single nucleotide polymorphism data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 358-367, February.
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