摘 要
本研究深入探讨了大数据环境下数据挖掘技术的应用与挑战。通过阐述数据挖掘技术的定义、目标、主要方法及其发展历程，构建了数据挖掘技术的理论基础。进而，在大数据背景下，我们分析了数据挖掘技术面临的关键技术，包括大数据预处理技术、并行与分布式技术、可视化技术以及算法与模型创新技术。面对大数据环境下数据量巨大、数据类型多样、数据质量不一以及数据安全与隐私保护等挑战，本研究提出了针对性的对策。采用高性能计算平台提高数据处理效率，统一数据集成工具整合多元数据源，改进数据预处理技术以应对数据质量问题，同时强化数据安全措施确保数据隐私。在大数据应用中，数据挖掘技术发挥了至关重要的作用。在商业智能领域，数据挖掘技术用于客户行为分析、市场趋势预测，为企业决策提供有力支持；在社交媒体分析中，该技术帮助实现舆情监控与分析、影响力分析，为舆论引导提供科学依据；在医疗健康领域，数据挖掘技术助力疾病预测与预防、医疗资源优化，为提升医疗服务质量提供了重要支持。本研究旨在全面分析大数据环境下数据挖掘技术的应用现状与挑战，提出相应的技术对策，为数据挖掘技术的进一步发展及其在大数据领域的应用提供理论参考和实践指导。
关键词：数据挖掘 大数据环境 并行与分布式技术
Abstract
This study deeply explores the application and challenges of data mining technology in the big data environment. By explaining the definition, goal, main method and development process of data mining technology, the theoretical basis of data mining technology is constructed. Furthermore, in the context of big data, we analyze the key technologies faced by data mining technology, including big data preprocessing technology, parallel and distributed technology, visualization technology, and algorithm and model innovation technology. Facing the challenges of large data volume, diverse data types, different data quality and data security and privacy protection, this study puts forward targeted countermeasures. Adopt high-performance computing platform to improve data processing efficiency, unify data integration tools to integrate multiple data sources, improve data preprocessing technology to deal with data quality problems, and strengthen data security measures to ensure data privacy. In the big data application, the data mining technology plays a vital role. In the field of business intelligence, data mining technology is used for customer behavior analysis and market trend prediction to provide strong support for enterprise decision-making; in social media analysis, this technology helps to realize public opinion monitoring and analysis and influence analysis, and provide scientific basis for public opinion guidance; in the field of medical and health care, data mining technology helps disease prediction and prevention and optimization of medical resources, and provides important support for improving medical service quality. The purpose of this study is to comprehensively analyze the application status and challenges of data mining technology in the environment of big data, put forward the corresponding technical countermeasures, and provide theoretical reference and practical guidance for the further development of data mining technology and its application in the field of big data.
Keyword:Data mining; big data environment Big data environment Parallel and distributed technology
目 录
1引言 1
2数据挖掘技术基础 1
2.1数据挖掘的定义与目标 1
2.2数据挖掘的主要方法 1
2.3数据挖掘技术的发展历程 2
3大数据环境下的数据挖掘技术 2
3.1大数据预处理技术 2
3.2大数据挖掘的并行与分布式技术 3
3.3大数据挖掘的可视化技术 3
3.4大数据挖掘的算法与模型创新技术 4
4数据环境下数据挖掘技术的挑战 4
4.1数据量巨大 4
4.2数据类型多样 4
4.3数据质量不一 5
4.4数据安全与隐私保护 6
5数据环境下数据挖掘技术的对策 6
5.1采用高性能计算平台 6
5.2统一数据集成工具 6
5.3生物降解塑料的循环利用与回收 7
5.4强化数据安全措施 7
6结论 7
参考文献 9
致谢 10
本研究深入探讨了大数据环境下数据挖掘技术的应用与挑战。通过阐述数据挖掘技术的定义、目标、主要方法及其发展历程，构建了数据挖掘技术的理论基础。进而，在大数据背景下，我们分析了数据挖掘技术面临的关键技术，包括大数据预处理技术、并行与分布式技术、可视化技术以及算法与模型创新技术。面对大数据环境下数据量巨大、数据类型多样、数据质量不一以及数据安全与隐私保护等挑战，本研究提出了针对性的对策。采用高性能计算平台提高数据处理效率，统一数据集成工具整合多元数据源，改进数据预处理技术以应对数据质量问题，同时强化数据安全措施确保数据隐私。在大数据应用中，数据挖掘技术发挥了至关重要的作用。在商业智能领域，数据挖掘技术用于客户行为分析、市场趋势预测，为企业决策提供有力支持；在社交媒体分析中，该技术帮助实现舆情监控与分析、影响力分析，为舆论引导提供科学依据；在医疗健康领域，数据挖掘技术助力疾病预测与预防、医疗资源优化，为提升医疗服务质量提供了重要支持。本研究旨在全面分析大数据环境下数据挖掘技术的应用现状与挑战，提出相应的技术对策，为数据挖掘技术的进一步发展及其在大数据领域的应用提供理论参考和实践指导。
关键词：数据挖掘 大数据环境 并行与分布式技术
Abstract
This study deeply explores the application and challenges of data mining technology in the big data environment. By explaining the definition, goal, main method and development process of data mining technology, the theoretical basis of data mining technology is constructed. Furthermore, in the context of big data, we analyze the key technologies faced by data mining technology, including big data preprocessing technology, parallel and distributed technology, visualization technology, and algorithm and model innovation technology. Facing the challenges of large data volume, diverse data types, different data quality and data security and privacy protection, this study puts forward targeted countermeasures. Adopt high-performance computing platform to improve data processing efficiency, unify data integration tools to integrate multiple data sources, improve data preprocessing technology to deal with data quality problems, and strengthen data security measures to ensure data privacy. In the big data application, the data mining technology plays a vital role. In the field of business intelligence, data mining technology is used for customer behavior analysis and market trend prediction to provide strong support for enterprise decision-making; in social media analysis, this technology helps to realize public opinion monitoring and analysis and influence analysis, and provide scientific basis for public opinion guidance; in the field of medical and health care, data mining technology helps disease prediction and prevention and optimization of medical resources, and provides important support for improving medical service quality. The purpose of this study is to comprehensively analyze the application status and challenges of data mining technology in the environment of big data, put forward the corresponding technical countermeasures, and provide theoretical reference and practical guidance for the further development of data mining technology and its application in the field of big data.
Keyword:Data mining; big data environment Big data environment Parallel and distributed technology
目 录
1引言 1
2数据挖掘技术基础 1
2.1数据挖掘的定义与目标 1
2.2数据挖掘的主要方法 1
2.3数据挖掘技术的发展历程 2
3大数据环境下的数据挖掘技术 2
3.1大数据预处理技术 2
3.2大数据挖掘的并行与分布式技术 3
3.3大数据挖掘的可视化技术 3
3.4大数据挖掘的算法与模型创新技术 4
4数据环境下数据挖掘技术的挑战 4
4.1数据量巨大 4
4.2数据类型多样 4
4.3数据质量不一 5
4.4数据安全与隐私保护 6
5数据环境下数据挖掘技术的对策 6
5.1采用高性能计算平台 6
5.2统一数据集成工具 6
5.3生物降解塑料的循环利用与回收 7
5.4强化数据安全措施 7
6结论 7
参考文献 9
致谢 10
