Frequent Genre Mining on Hit and Viral Songs - P. Oliveira, Gabriel; Moro, Mirella M. (Journal of Information and Data Management, 2025)
Music is a dynamic cultural industry that has produced large volumes of data since the beginning of streaming services. Understanding such data provides valuable insights into music consumption, and helps identifying emerging trends and fostering creativity within the music industry. Nowadays, combining different genres has become a common practice to promote new music and reach new audiences. Given the diversity of combinations between all genres, predictive and descriptive analyses are very challenging. This work aims to explore the relationship between genre combinations and music popularity by mining frequent patterns in hit and viral songs across global and regional markets. We extend previous work by incorporating viral songs into the analysis, thus strengthening the comparative analysis of musical popularity's interconnected facets. We use the Apriori algorithm to mine genre patterns and association rules that reveal how music genres combine with each other in each market. Our findings reveal significant differences in popular genres across regions and highlight the dynamic nature of genre-blending in modern music. In addition, we are able to use such patterns to identify and recommend promising genre combinations for such markets through the association rules.