Parallel computing algorithms and efficient data mining model design for large-scale data sets

With the rapid growth of large-scale datasets, traditional data processing and mining methods face efficiency bottlenecks. Parallel computing algorithms provide a feasible technical path to solve this problem, which significantly improves the data processing speed and analysis accuracy through efficient resource scheduling and distributed computing. This paper provides an in-depth discussion of parallel computing algorithms for large-scale datasets and their applications in efficient data mining models. It introduces the basic concepts and application scenarios of parallel computing algorithms in large-scale datasets, and analyzes the advantages of this technology in enhancing data processing efficiency. Aiming at the complexity of data mining tasks, an efficient data mining model design combined with parallel computing is proposed, focusing on data preprocessing, feature selection, distributed data mining algorithms and their optimization strategies. Combined with practical application cases, the advantages of combining parallel computing and data mining model are demonstrated, and the prospect of its wide application in the future big data analysis field is envisioned. This study provides new ideas and methods for efficient processing and analysis of large-scale data sets, and is of great significance in guiding the research and practice in related fields.