Exploring the potential of digital algorithms for visualizing corpus-based data from naturally occurring language texts

Researchers in fields like computer science, artificial intelligence and machine learning actively study clustering algorithms such as K-means, DBScan, and BIRCH. However, applying these algorithms requires not only technical proficiency but also careful attention to text preprocessing, normalization, and selecting appropriate vectorization models. Today, it is crucial to integrate these digital algorithms into corpus-based studies, with a particular focus on the algorithms used for visualizing data. The main objective of our study is to gain a deeper understanding of how to visualize normalized data in corpusbased research.