2022/05/08 by Jiajun Shen, Shen, Jiajun
Computer Science · Decision Sciences · #Big Data Technologies and Applications #Data Mining Algorithms and Applications #Distributed #FOS: Computer and information sciences #Machine Learning and Data Classification #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2206.07090
openalex publication_date 2022/05/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the rapid development of big data technologies, how to dig out useful information from massive data becomes an essential problem. However, using machine learning algorithms to analyze large data may be time-consuming and inefficient on the traditional single machine. To solve these problems, this paper has made some research on the parallelization of several classic machine learning algorithms respectively on the single machine and the big data platform Spark. We compare the runtime and efficiency of traditional machine learning algorithms with parallelized machine learning algorithms respectively on the single machine and Spark platform. The research results have shown significant improvement in runtime and efficiency of parallelized machine learning algorithms.