Research on the Application of Efficient Parallel Algorithm in Large-Scale Data Processing

Traditional data mining methods are inefficient in dealing with huge and diverse data, and it is difficult to meet the requirements of real-time and accuracy. Parallel algorithm significantly improves the efficiency of data processing by using computing resources such as multi-core processors and distributed systems. This paper analyzes the principle, design method and application scenario of parallel algorithm, and pays attention to its performance optimization and integration innovation with other technologies. The research focuses on parallel clustering algorithm and parallel association rule mining algorithm, which effectively solve the computing bottleneck problem in big data mining by decomposing tasks, parallel execution and merging results. In addition, this paper also discusses the application examples of parallel algorithm in social network analysis and supermarket shopping data analysis, and shows the advantages of parallel algorithm in improving computing efficiency, reducing memory requirements and enhancing scalability. Finally, this paper discusses the integration of parallel algorithm with deep learning training and distributed optimization technology, and the application of these technologies in deep learning framework, which provides new ideas and tools for the development of big data mining technology. Through this study, we can provide valuable reference for researchers and practitioners in the field of big data mining, promote the progress of related technologies, and provide more efficient and accurate data support for smart city construction, business intelligence, medical health, scientific research and other fields.