Here’s a look at categorical data, why it’s hard to wrangle, and how it could be useful. Categorical Data 101. There are two main types of data: categorical and numerical. Numerical data, as the name ...

The technique presented in this article to cluster mixed categorical and numeric data can also be used to cluster strictly categorical data using k-means. The demo uses k = 3 clusters, which is more ...

Numerical and Categorical Attributes Data Clustering Using K-Modes and Fuzzy K-Modes Most of the existing clustering approaches are applicable to purely numerical or categorical data only, but not ...

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

Conclusion. Encoding categorical data is a crucial step in data preprocessing. By converting categorical data into a numeric format, machine learning models can interpret and work more effectively.

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).

This short course will introduce the concept, theory, and application of GLM. Moreover, we will discuss some techniques commonly used in categorical data analysis, such as contingency table analysis, ...