大数据环境下快速数据挖掘算法的应用分析  

Application Analysis of Fast Data Mining Algorithm in Big Data Environment

作　　者：王文婷 WANG Wenting(Chongqing College of International Business and Economics,Chongqing 401520,China)

机构地区：[1]重庆对外经贸学院,重庆401520

出　　处：《通信电源技术》2025年第4期195-198,共4页Telecom Power Technology

摘　　要：在大数据环境下,快速数据挖掘算法是从海量数据中提取有价值信息的关键技术。文章分析了几种主流快速数据挖掘算法的设计及其优化应用,重点探讨了频繁模式增长(Frequent Pattern-Growth,FP-Growth)、随机森林与K-Means++等算法的改进及其在商业、医疗、金融以及工业等领域中的实际应用。通过快速数据挖掘算法的应用,不仅能够快速适应多样化的大数据场景,有效提升数据分析的速度和准确性,还成功推动了商业、医疗、金融及工业领域等行业市场、管理和生产的变革,以及各行各业的智能化转型。研究表明,快速数据挖掘算法具有广泛的实用价值和深远的创新意义,为大规模数据分析与处理提供了有效的解决方案。In the big data environment,fast data mining algorithm is a key technology to extract valuable information from massive data.This paper analyzes the design and optimization application of several mainstream fast data mining algorithms,focusing on the improvement of Frequent Pattern-Growth(FP-Growth),Random forest and K-Means++algorithms and their practical applications in commercial,medical,financial and industrial fields.Through the application of fast data mining algorithms,it can not only quickly adapt to diverse big data scenarios,effectively improve the speed and accuracy of data analysis,but also successfully promote the market,management and production changes in industries such as business,medical,financial and industrial fields,and promote the intelligent transformation of all walks of life.The research shows that the fast data mining algorithm has a wide range of practical value and far-reaching innovative significance,and provides an effective solution for large-scale data analysis and processing.

关 键 词：大数据 快速数据挖掘 频繁模式增长(FP-Growth) 随机森林 K-Means++ 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]