TY  - JOUR
AU  - Andry, Johanes Fernandes 
AU  - Sibarani, Riama 
AU  - Yefta, Vlavio Nathanael 
PY  - 2023
TI  - Analysis of Big Data Football Club Market Value Using K-Means and Linear Regression Mining Methods
JF  - Journal of Computer Science
VL  - 19
IS  - 2
DO  - 10.3844/jcssp.2023.286.294
UR  - https://thescipub.com/abstract/jcssp.2023.286.294
AB  - Football clubs store a lot of data about their players, squad, transfer market, and market value, so big data is needed to process this data. This data can be analyzed to gain insight into the club's market value. Changes in player performance, age, and squad size can be analyzed using statistical methods. These digital data will provide information about the club's market value. Data mining is the process of analyzing data using various methods to produce useful information. The software application used in this data analysis is RapidMiner Studio, which is one of the best data mining tools. The purpose of this research is to analyze football clubs' market value according to squad size, players' value, and league. This study will use the clustering K-means and linear regression methods. The results of this study can be used by those who want to invest money in football clubs. An investor can use this data to predict and make decisions about whether to invest in specific clubs and leagues.