Data Mining Techniques in Blockchain Using Machine Learning Algorithms

Abstract

The rapid advancement of blockchain technology has generated an enormous volume of complex transaction data, creating significant challenges in data analysis, particularly in terms of scalability, noise, and anonymity. This study aims to identify effective data mining techniques and implement machine learning algorithms to enhance the performance of blockchain data analysis. A quantitative approach was employed by utilizing data mining techniques and machine learning algorithms, including Random Forest, K-Means, and Neural Network. These methods were applied to blockchain datasets obtained from Ethereum, Bitcoin, and OpenSea through several stages, namely preprocessing, feature engineering, model training, and evaluation using accuracy, precision, recall, F1-score, and Root Mean Square Error metrics. The results indicate that Random Forest demonstrates stable performance with high classification accuracy, Neural Network excel at capturing complex patterns in non-linear data, while K-Means is effective in identifying patterns through clustering. These findings suggest that each algorithm offers distinct advantages depending on the characteristics of the data and the objectives of the analysis. This study concludes that the integration of data mining techniques and machine learning algorithms can significantly improve the effectiveness of blockchain data analysis compared to traditional methods. Furthermore, the proposed integrated framework can serve as a reference for the future development of blockchain-based data analytics systems. Rather than providing a quantitative benchmark against conventional analytical approaches, this study proposes a standardized methodological framework intended to support consistent implementation, evaluation, and future comparative validation across heterogeneous blockchain analytics applications.