Comparative analysis of clustering textual and numerical data using the K-Means algorithm

This paper presents a comparative analysis of the application of the K-Means clustering algorithm on two different types of data - textual and numerical. The aim of the research was to examine the reliability, stability, and interpretability of the results when the same algorithm is applied to semantically diverse datasets. The textual data were taken from the articles of the Criminal Code of the Republic of Serbia, where clustering was performed after preprocessing and TF-IDF vectorization. The numerical data refer to traffic accident statistics from 2015 to 2021, analyzing parameters such as the number of property-damage-only accidents, the number of injured persons, and the number of fatalities. The results showed that clustering on textual data produced a relatively clear separation of thematic groups of articles, but with a moderate silhouette coefficient value due to a high degree of semantic similarity among documents. On the other hand, clustering on numerical data demonstrated a more stable structure, where the optimal number of clusters was two, indicating the possibility of distinguishing periods with different intensity and severity of traffic accidents. It was concluded that the K-Means algorithm provides more reliable and interpretable results for numerical data, while in the case of textual data, it requires more precise vector space modeling and possibly the application of semantic models such as Word2Vec or BERT. The paper serves as a basis for further research in the field of integrating machine learning techniques for analyzing heterogeneous data sources.