数据挖掘、大数据分析和机器学习方法综述

Francisco Pedro
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引用次数: 0

摘要

经济全球化的现象导致了各领域产业的迅速发展。因此,大数据技术引起了越来越多的兴趣。网络数据的生成正以前所未有的速度发生,需要对大量数据进行智能处理。为了充分利用这些数据的内在价值,机器学习技术的实施势在必行。在庞大的数据环境中,机器学习的目标是识别隐藏在动态、可变、多源异构数据中的特定规则,最终目标是最大化数据的价值。为了在复杂的动态数据集中识别相关的相关性,大数据技术和机器学习算法的集成是必不可少的。随后,基于计算机的数据挖掘可以用来提取有价值的研究见解。本研究对深度学习与传统数据挖掘和机器学习技术进行了比较分析。它对传统方法的优点和局限性进行了比较评估。此外,该研究还介绍了企业的需求,他们的系统和数据,他们面临的IT挑战,以及大数据在扩展服务基础设施中的作用。本研究分析了深度学习(包括机器学习和传统数据挖掘技术)在大数据分析环境中应用的可能性和相关问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review of Data Mining, Big Data Analytics and Machine Learning Approaches
The phenomenon of economic globalization has led to the swift advancement of industries across diverse domains. Consequently, big data technology has garnered increasing interest. The generation of network data is occurring at an unparalleled pace, necessitating the intelligent processing of vast amounts of data. To fully leverage the value inherent in this data, the implementation of machine learning techniques is imperative. The objective of machine learning in a vast data setting is to identify particular rules that are concealed within dynamic, variable, multi-origin heterogeneous data, with the ultimate aim of maximizing the value of the data. The integration of big data technology and machine learning algorithms is imperative in order to identify pertinent correlations within intricate and dynamic datasets. Subsequently, computer-based data mining can be utilized to extract valuable research insights. The present study undertakes an analysis of deep learning in comparison to conventional data mining and machine learning techniques. It conducts a comparative assessment of the strengths and limitations of the traditional methods. Additionally, the study introduces the requirements of enterprises, their systems and data, the IT challenges they face, and the role of Big Data in an extended service infrastructure. This study presents an analysis of the probability and issues associated with the utilization of deep learning, including machine learning and traditional data mining techniques, in the big data analytics context.
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