Music genre influence and artist similarity based on data analysis

作     者：Yuhui Xiao Zhilin He Huming Liao Kangjian Huang Jianping Zhao Yuchen Wu 

作者机构：Nuclear engineering and technology Chengdu University of Technology ChengDu SiChuan Province 610059 China Mathematics and Applied Mathematics Chengdu University of Technology ChengDu SiChuan Province610059 China Department of Computer Science and Technology Chengdu University of Technology ChengDu SiChuan Province 610059 China Material forming and control engineering (Welding and inspection) Southwest Petroleum University ChengDu SiChuan Province 610599 China Physics Southwest University ChongQing City 400715 China 

出 版 物：《Journal of Physics: Conference Series》 

年 卷 期：2021年第1903卷第1期

学科分类：07[理学] 0702[理学-物理学] 

摘      要：Based on related data and cluster analysis, this paper creates a mathematical model of similarity measurement based on the data set, and finally obtains the influence of different music genres on their followers and the similarity of artists of different musical genres. Here, since the study is about the similarity of music, it is divided into 2 categories according to the provided music characteristics and related indicators of music type. The data is standardized and normalized. Because there is no clear indicator to measure the similarity of music, we use spss to perform K-means clustering analysis. Here, since the similarity of music is studied, it is built on the music characteristics and music provided. Types of related indicators divide it into two categories for analysis. In order to determine the reliability of the model, this article uses meaningful learning to train 70% of the previous data, 30% to test, and finally establishes a complete mathematical model.