The simple answer is that using categorical data with today’s tools is complex, and most data scientists aren’t trained to use it. Figuring out how to use categorical data will help companies solve ...

This article presents a technique for clustering mixed categorical and numeric data using standard k-means clustering implemented using the C# language. Briefly, the source mixed data is preprocessed ...

Data analysis is a fundamental process in any project. However, data can be lumped into different types, with categorical and ...

Most of the existing clustering approaches are applicable to purely numerical or categorical data only, but not the both. In general, it is a nontrivial task to perform clustering on mixed data ...

Machine learning algorithms, on the other hand, mainly operate on numeric data. Therefore, encapsulating categorical variables into numerical form is paramount for model accuracy. This allows ...

Beyond achieving technical excellence, the study underscores the practical utility of explainable AI in flood risk management ...

Categorical and numerical data are common in dental research and they may be analysed, presented or summarised by a variety of methods including: Proportions (eg, percentages).

Categorical data analysis, including contingency table analysis, measures of association, tests of independence, tests of symmetry. How to use R to fit GLMs using real data.