数据挖掘的影响与挑战:全面分析

Chandrakant D. Prajapati, Asha K. Patel, Dr. Krupa J. Bhavsar
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引用次数: 0

摘要

数据挖掘是一个多学科领域,侧重于从大量数据集中提取有价值的见解和模式。它重点介绍了如何利用统计分析、机器学习和模式识别技术来发现数据中隐藏的关系和趋势。论文强调了数据挖掘作为从大型数据库中提取预测信息的强大技术的重要意义,使企业能够对关键数据进行优先排序。它展示了数据挖掘工具如何预测未来趋势,从而帮助企业做出积极主动、以知识为导向的决策。此外,它还讨论了数据挖掘相对于回顾性工具的优越性,提供自动化的前瞻性分析,以高效解决复杂的业务问题。它揭示了隐藏的模式和预测信息,超出了人类的预期。本文还讨论了数据挖掘的核心概念、遇到的挑战、数据分析技术及其对各个领域的深远影响。本文全面概述了数据挖掘在现代数据驱动决策过程中的重要性、应用和变革潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact and Challenges of Data Mining : A Comprehensive Analysis
This review paper provides a concise overview of Data Mining, a multidisciplinary field focused on extracting valuable insights and patterns from extensive datasets. It highlights the use of statistical analysis, machine learning, and pattern recognition techniques to discover hidden relationships and trends within data. The paper emphasizes data mining's significance as a powerful technology that extracts predictive information from large databases, enabling businesses to prioritize crucial data. It showcases how data mining tools predict future trends, empowering proactive, knowledge-driven decision-making. Furthermore, it discusses the superiority of data mining over retrospective tools, offering automated, prospective analyses to resolve complex business questions efficiently. It uncovers hidden patterns and predictive information beyond human expectations. The core concepts of data mining encountered challenges, data analysis techniques, and their profound impact on various domains are also addressed in this paper. The proposed paper offers a comprehensive overview of data mining's importance, applications, and transformative potential in modern data-driven decision-making processes.
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