Mining Exceptional Genre Patterns on Hit Songs


The music industry has always been complex and competitive. 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. Here, our goal is to mine frequent and exceptional patterns in music collaborations that have achieved success in both global and regional markets. We use the Apriori algorithm to mine genre patterns and association rules that reveal how music genres combine with each other in each market. The results show significant differences in the behavior of each market and a strong influence of the regional factor on musical success. In addition, we are able to use such patterns to identify and recommend promising genre combinations for such markets through the association rules.

Palavras-chave: hit song science, music data mining, music information retrieval, musical genres


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OLIVEIRA, Gabriel P.; MORO, Mirella M.. Mining Exceptional Genre Patterns on Hit Songs. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 11. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 97-104. ISSN 2763-8944. DOI: