Automatic Group Labeling with Decision Trees: A Comparative Approach
Manoel Messias P. Medeiros, Daniel de Sousa Luz, Rodrigo De Melo Souza Veras · 2024
The exponential growth in data volume demands efficient data analysis techniques, with data clustering being crucial but interpretation often posing a challenge. Automated group labeling using decision trees can alleviate this issue. This study compares four decision tree algorithms for automated group labeling, demonstrating that algorithm choice significantly influences performance. CHAID outperforms other algorithms in the Iris and Seeds datasets, while C4.5 excels in the Wine and Glass datasets. The proposed model’s validity is confirmed, highlighting the importance of careful algorithm selection. These findings underscore the potential of automated group labeling models and emphasize the need for further research to refine and expand their applications across various domains.