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Categorical Data Analysis for High-Dimensional Sparse Gene Expression Data [PDF]
BioTech, 2023 Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression analysis for such scenarios ...
Niloufar Dousti Mousavi +2 more
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Bayesian Gower agreement for categorical data [PDF]
Scientific ReportsIn this work I present two methods for measuring agreement in nominal and ordinal data. The measures, which employ Gower-type distances, are simple, intuitive, and easy to compute for any number of units and any number of coders. Influential units and/or
John Hughes
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A Novel Boolean Kernels Family for Categorical Data [PDF]
Entropy, 2018 Kernel based classifiers, such as SVM, are considered state-of-the-art algorithms and are widely used on many classification tasks. However, this kind of methods are hardly interpretable and for this reason they are often considered as black-box models ...
Mirko Polato +2 more
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Multi-Objective Evolutionary Rule-Based Classification with Categorical Data [PDF]
Entropy, 2018 The ease of interpretation of a classification model is essential for the task of validating it. Sometimes it is required to clearly explain the classification process of a model’s predictions. Models which are inherently easier to interpret can be
Fernando Jiménez +4 more
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CDE++: Learning Categorical Data Embedding by Enhancing Heterogeneous Feature Value Coupling Relationships [PDF]
Entropy, 2020 Categorical data are ubiquitous in machine learning tasks, and the representation of categorical data plays an important role in the learning performance.
Bin Dong, Songlei Jian, Ke Zuo
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The VGAM Package for Categorical Data Analysis
Journal of Statistical Software, 2010 Classical categorical regression models such as the multinomial logit and proportional odds models are shown to be readily handled by the vector generalized linear and additive model (VGLM/VGAM) framework. Additionally, there are natural extensions, such
Thomas W. Yee
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Wrangling categorical data in R
The American Statistician, 2017 Data wrangling is a critical foundation of data science, and wrangling of categorical data is an important component of this process. However, categorical data can introduce unique issues in data wrangling, particularly in real-world settings with collaborators and periodically-updated dynamic data.
Amelia McNamara, Nicholas J. Horton
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Fuzzy Soft Set Clustering for Categorical Data [PDF]
JOIV: International Journal on Informatics VisualizationCategorical data clustering is difficult because categorical data lacks natural order and can comprise groups of data only related to specific dimensions. Conventional clustering, such as k-means, cannot be openly used to categorical data.
Iwan Tri Riyadi Yanto +5 more
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Citation: 'categorical data' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.10045 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms.
John Kloke, Joseph McKean
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Missing Data Imputation for Categorical Variables [PDF]
Statistika: Statistics and Economy Journal, 2022 Dealing with missing data is a crucial part of everyday data analysis. The IMIC algorithm is a missing data imputation method that can handle mixed numerical and categorical datasets. However, the categorical data are crucial for this work.
Jaroslav Horníček, Hana Řezanková
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