Partially exchangeable datasets are characterized by observations grouped into known, heterogeneous units. The recently developed Common Atoms Model (CAM) is a Bayesian nonparametric technique suited for analyzing this type of data. CAM induces a two-layered clustering structure: one across observations and another across units. In particular, the units are clustered according to their distri- butional similarities. In this article, we illustrate the versatility of CAM with an application to an openly available Spotify dataset. The dataset contains quantitative audio features for a large number of songs grouped by artists. After describing the data preprocessing steps, we employ CAM to group the Spotify artists according to the distributions of the energy of their songs.

Denti, F., Camerlenghi, F., Guindani, M., Mira, A., Clustering artists based on the energy distributions of their songs on Spotify via the Common Atoms Model, in Book of Short Paper 2022, (Caserta, 22-24 June 2022), Pearson, Caserta 2022: 121-126 [https://hdl.handle.net/10807/221887]

Clustering artists based on the energy distributions of their songs on Spotify via the Common Atoms Model

Denti, Francesco
Primo
;
2022

Abstract

Partially exchangeable datasets are characterized by observations grouped into known, heterogeneous units. The recently developed Common Atoms Model (CAM) is a Bayesian nonparametric technique suited for analyzing this type of data. CAM induces a two-layered clustering structure: one across observations and another across units. In particular, the units are clustered according to their distri- butional similarities. In this article, we illustrate the versatility of CAM with an application to an openly available Spotify dataset. The dataset contains quantitative audio features for a large number of songs grouped by artists. After describing the data preprocessing steps, we employ CAM to group the Spotify artists according to the distributions of the energy of their songs.
Inglese
Book of Short Paper 2022
SIS 2022
Caserta
22-giu-2022
24-giu-2022
9788891932310
Pearson
Denti, F., Camerlenghi, F., Guindani, M., Mira, A., Clustering artists based on the energy distributions of their songs on Spotify via the Common Atoms Model, in Book of Short Paper 2022, (Caserta, 22-24 June 2022), Pearson, Caserta 2022: 121-126 [https://hdl.handle.net/10807/221887]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/221887
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