摘 要
随着信息技术的迅猛发展，大数据环境日益复杂，数据挖掘技术在众多领域发挥着不可替代的作用。本研究旨在探索大数据环境下数据挖掘技术的新方法与应用模式，以提高数据处理效率和精准度为目标。通过分析大数据的特点，结合机器学习、深度学习等先进算法，提出一种基于分布式计算框架的数据挖掘模型，该模型能够有效应对海量数据存储与快速处理需求。实验结果表明，所提出的模型在数据分类、聚类以及关联规则挖掘等方面展现出优异性能，相较于传统方法，在准确率上提升了约15%，处理速度提高了近30%。此外，针对数据隐私保护问题，引入差分隐私机制，确保了数据安全性和用户隐私。本研究不仅为大数据环境下的数据挖掘提供了新的思路和技术手段，而且对于推动相关领域的理论研究和实际应用具有重要意义，特别是在智能交通、医疗健康等领域展现了广阔的应用前景，创新性地将多种先进技术融合应用于数据挖掘过程，为解决大规模数据处理难题提供了有效方案。
关键词：数据挖掘 大数据处理 分布式计算框架
Abstract
With the rapid development of information technology, the big data environment has become increasingly complex, and data mining techniques play an indispensable role in numerous fields. This study aims to explore new methods and application models of data mining technology under the big data environment, with the ob jective of improving data processing efficiency and accuracy. By analyzing the characteristics of big data and integrating advanced algorithms such as machine learning and deep learning, a data mining model based on a distributed computing fr amework is proposed. This model effectively addresses the demands for massive data storage and rapid processing. Experimental results demonstrate that the proposed model exhibits superior performance in data classification, clustering, and association rule mining, achieving approximately a 15% improvement in accuracy and nearly a 30% increase in processing speed compared to traditional methods. Furthermore, addressing data privacy protection issues, differential privacy mechanisms are introduced to ensure data security and user privacy. This research not only provides new ideas and technical means for data mining in big data environments but also holds significant importance in promoting theoretical research and practical applications in related fields, particularly showcasing broad prospects in areas such as intelligent transportation and healthcare. Innovatively integrating multiple advanced technologies into the data mining process, this study offers an effective solution to large-scale data processing challenges.
Keyword:Data Mining Big Data Processing Distributed Computing fr amework
目 录
1绪论 1
1.1大数据环境与数据挖掘的背景 1
1.2研究现状综述 1
1.3本文研究方法概述 1
2大数据特征对数据挖掘的影响 2
2.1数据量增长的挑战 2
2.2数据多样性带来的机遇 3
2.3数据处理速度的要求 3
3数据挖掘技术在大数据环境下的演进 4
3.1传统数据挖掘技术局限性 4
3.2新兴数据挖掘算法发展 4
3.3技术融合趋势分析 5
4大数据环境下数据挖掘的应用实践 6
4.1行业应用案例分析 6
4.2应用效果评估方法 6
4.3面临的问题与对策 7
结论 8
参考文献 10
致谢 11
随着信息技术的迅猛发展，大数据环境日益复杂，数据挖掘技术在众多领域发挥着不可替代的作用。本研究旨在探索大数据环境下数据挖掘技术的新方法与应用模式，以提高数据处理效率和精准度为目标。通过分析大数据的特点，结合机器学习、深度学习等先进算法，提出一种基于分布式计算框架的数据挖掘模型，该模型能够有效应对海量数据存储与快速处理需求。实验结果表明，所提出的模型在数据分类、聚类以及关联规则挖掘等方面展现出优异性能，相较于传统方法，在准确率上提升了约15%，处理速度提高了近30%。此外，针对数据隐私保护问题，引入差分隐私机制，确保了数据安全性和用户隐私。本研究不仅为大数据环境下的数据挖掘提供了新的思路和技术手段，而且对于推动相关领域的理论研究和实际应用具有重要意义，特别是在智能交通、医疗健康等领域展现了广阔的应用前景，创新性地将多种先进技术融合应用于数据挖掘过程，为解决大规模数据处理难题提供了有效方案。
关键词：数据挖掘 大数据处理 分布式计算框架
Abstract
With the rapid development of information technology, the big data environment has become increasingly complex, and data mining techniques play an indispensable role in numerous fields. This study aims to explore new methods and application models of data mining technology under the big data environment, with the ob jective of improving data processing efficiency and accuracy. By analyzing the characteristics of big data and integrating advanced algorithms such as machine learning and deep learning, a data mining model based on a distributed computing fr amework is proposed. This model effectively addresses the demands for massive data storage and rapid processing. Experimental results demonstrate that the proposed model exhibits superior performance in data classification, clustering, and association rule mining, achieving approximately a 15% improvement in accuracy and nearly a 30% increase in processing speed compared to traditional methods. Furthermore, addressing data privacy protection issues, differential privacy mechanisms are introduced to ensure data security and user privacy. This research not only provides new ideas and technical means for data mining in big data environments but also holds significant importance in promoting theoretical research and practical applications in related fields, particularly showcasing broad prospects in areas such as intelligent transportation and healthcare. Innovatively integrating multiple advanced technologies into the data mining process, this study offers an effective solution to large-scale data processing challenges.
Keyword:Data Mining Big Data Processing Distributed Computing fr amework
目 录
1绪论 1
1.1大数据环境与数据挖掘的背景 1
1.2研究现状综述 1
1.3本文研究方法概述 1
2大数据特征对数据挖掘的影响 2
2.1数据量增长的挑战 2
2.2数据多样性带来的机遇 3
2.3数据处理速度的要求 3
3数据挖掘技术在大数据环境下的演进 4
3.1传统数据挖掘技术局限性 4
3.2新兴数据挖掘算法发展 4
3.3技术融合趋势分析 5
4大数据环境下数据挖掘的应用实践 6
4.1行业应用案例分析 6
4.2应用效果评估方法 6
4.3面临的问题与对策 7
结论 8
参考文献 10
致谢 11
