On mathematical optimization for clustering categories in contingency tables

Many applications in data analysis study whether two categorical variables are independent using a function of the entries of their contingency table. Often, the categories of the variables, associated with the rows and columns of the table, are grouped, yielding a less granular representation of th...

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Veröffentlicht in:	Advances in data analysis and classification Jg. 17; H. 2; S. 407 - 429
Hauptverfasser:	Carrizosa, Emilio, Guerrero, Vanesa, Romero Morales, Dolores
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023 Springer Nature B.V
Schlagworte:	Categories Chemistry and Earth Sciences Chi-square test Clustering Computer Science Constraints Contingency Contingency tables Data analysis Data Mining and Knowledge Discovery Data science Dependent variables Economics Finance Health Sciences Humanities Independent variables Insurance Law Management Mathematics and Statistics Medicine Optimization Physics Probability distribution Regular Article Sparsity Statistical analysis Statistical Theory and Methods Statistics Statistics for Business Statistics for Engineering Statistics for Life Sciences Statistics for Social Sciences Variables 90C90 90C27 Relational constraints Clustering Mathematical optimization Contingency tables
ISSN:	1862-5347, 1862-5355
Online-Zugang:	Volltext
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