Clustering objects on subsets of attributes. A new procedure is proposed for clustering attribute value data. When used in conjunction with conventional distance-based clustering algorithms this procedure encourages those algorithms to detect automatically subgroups of objects that preferentially cluster on subsets of the attribute variables rather than on all of them simultaneously. The relevant attribute subsets for each individual cluster can be different and partially (or completely) overlap with those of other clusters. Enhancements for increasing sensitivity for detecting especially low cardinality groups clustering on a small subset of variables are discussed. Applications in different domains, including gene expression arrays, are presented.

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  1. de A. T. de Carvalho, Francisco; Balzanella, Antonio; Irpino, Antonio; Verde, Rosanna: Co-clustering algorithms for distributional data with automated variable weighting (2021)
  2. Doo, Woojin; Kim, Heeyoung: Bayesian variable selection in clustering high-dimensional data via a mixture of finite mixtures (2021)
  3. Moran, Gemma E.; Ročková, Veronika; George, Edward I.: Spike-and-slab Lasso biclustering (2021)
  4. Zhang, Wen; Wang, Qiang; Yoshida, Taketoshi; Li, Jian: RP-LGMC: rating prediction based on local and global information with matrix clustering (2021)
  5. Hennig, Christian; Viroli, Cinzia; Anderlucci, Laura: Quantile-based clustering (2019)
  6. Yuan, Beibei; Heiser, Willem; De Rooij, Mark: The (\delta)-machine: classification based on distances towards prototypes (2019)
  7. Amiri, Saeid; Clarke, Bertrand S.; Clarke, Jennifer L.: Clustering categorical data via ensembling dissimilarity matrices (2018)
  8. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  9. Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
  10. Jackson, Adrian; Campobasso, M. Sergio; Drofelnik, Jernej: Load balance and parallel I/O: optimising COSA for large simulations (2018)
  11. Sepúlveda, E.; Dowd, P. A.; Xu, C.: Fuzzy clustering with spatial correction and its application to geometallurgical domaining (2018)
  12. Arias-Castro, Ery; Pu, Xiao: A simple approach to sparse clustering (2017)
  13. Banerjee, Trambak; Mukherjee, Gourab; Radchenko, Peter: Feature screening in large scale cluster analysis (2017)
  14. Floriello, Davide; Vitelli, Valeria: Sparse clustering of functional data (2017)
  15. Gaynor, Sheila; Bair, Eric: Identification of relevant subtypes via preweighted sparse clustering (2017)
  16. Kampert, Maarten M.; Meulman, Jacqueline J.; Friedman, Jerome H.: rCOSA: a software package for clustering objects on subsets of attributes (2017)
  17. Marbac, Matthieu; Sedki, Mohammed: Variable selection for model-based clustering using the integrated complete-data likelihood (2017)
  18. McArtor, Daniel B.; Lubke, Gitta H.; Bergeman, C. S.: Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic (2017)
  19. Deng, Zhaohong; Choi, Kup-Sze; Jiang, Yizhang; Wang, Jun; Wang, Shitong: A survey on soft subspace clustering (2016)
  20. Foss, Alex; Markatou, Marianthi; Ray, Bonnie; Heching, Aliza: A semiparametric method for clustering mixed data (2016)

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