Music genre influence and artist similarity based on data analysis. Issue 1 (April 2021)

• Record Type: Journal Article
• Authors: Xiao, Yuhui; He, Zhilin; Liao, Huming; Huang, Kangjian; Zhao, Jianping; Wu, Yuchen
• Is Part Of: Journal of physics. Volume 1903:Issue 1(2021)
• Publication Date: 2021-04
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1903/1/012007
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: 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.