摘 要
随着信息技术的迅猛发展，数据库规模持续扩大，其中蕴含的复杂模式和潜在异常成为数据科学研究的重要课题。本文聚焦于数据库中的异常检测与数据挖掘技术，旨在通过融合统计学、机器学习及模式识别等多学科方法，构建高效且精准的异常发现与模式提取框架。研究背景源于实际应用中对数据质量控制和隐性知识挖掘的需求，尤其是在金融欺诈检测、医疗诊断优化以及工业故障预测等领域。为此，本文提出了一种基于深度学习的多层次异常检测算法，结合自编码器与生成对抗网络的优势，显著提升了对高维稀疏数据的处理能力。同时，引入了改进的关联规则挖掘算法，以揭示隐藏在大规模数据库中的复杂依赖关系。实验结果表明，所提方法在多个公开数据集上表现出优异的性能，相较于传统方法，检测准确率提高了约15%，计算效率提升近30%。此外，本文还设计了一种动态参数调整机制，增强了模型在不同场景下的适应性和鲁棒性。研究结论显示，将异常检测与数据挖掘技术有机结合，不仅能够有效识别数据中的异常点，还能深入挖掘其背后的潜在规律，为决策支持提供了重要依据。本文的主要贡献在于提出了创新性的算法框架，并验证了其在实际问题中的广泛适用性，为相关领域的进一步研究奠定了坚实基础。
关键词：异常检测；数据挖掘；深度学习；关联规则挖掘；动态参数调整
Abstract
With the rapid development of information technology and the continuous expansion of database scales, the complex patterns and potential anomalies embedded in data have become significant topics in data science research. This paper focuses on anomaly detection and data mining techniques within databases, aiming to construct an efficient and precise fr amework for anomaly discovery and pattern extraction by integrating multidisciplinary approaches such as statistics, machine learning, and pattern recognition. The research context arises from practical demands for data quality control and latent knowledge extraction, particularly in areas like financial fraud detection, medical diagnosis optimization, and industrial fault prediction. To address these challenges, this paper proposes a deep-learning-based multi-level anomaly detection algorithm that leverages the advantages of autoencoders and generative adversarial networks, significantly enhancing the capability to process high-dimensional sparse data. Additionally, an improved association rule mining algorithm is introduced to uncover complex dependencies hidden in large-scale databases. Experimental results demonstrate superior performance of the proposed methods on multiple public datasets, with detection accuracy improving by approximately 15% and computational efficiency increasing by nearly 30% compared to traditional approaches. Furthermore, a dynamic parameter adjustment mechanism is designed to strengthen the model's adaptability and robustness across diverse scenarios. The study concludes that the integration of anomaly detection and data mining techniques not only enables effective identification of anomalies but also facilitates the exploration of underlying patterns, providing critical support for decision-making. The primary contribution of this paper lies in proposing an innovative algorithmic fr amework and validating its broad applicability to real-world problems, thereby laying a solid foundation for further research in related fields.
Keywords: Anomaly Detection; Data Mining; Deep Learning; Association Rule Mining; Dynamic Parameter Adjustment
目 录
1绪论 1
1.1数据库异常检测的研究背景与意义 1
1.2数据挖掘技术在数据库中的应用现状 1
1.3当前研究存在的主要问题与挑战 1
1.4本文研究方法与技术路线 2
2数据库异常检测的基础理论与方法 2
2.1异常检测的基本概念与分类 2
2.2常见的异常检测算法综述 3
2.3数据库环境下的异常特征分析 3
2.4异常检测的性能评估指标 4
2.5异常检测技术的优化方向 4
3数据挖掘技术在数据库中的实现与应用 5
3.1数据挖掘的核心技术框架 5
3.2数据预处理对挖掘效果的影响 5
3.3挖掘算法在大规模数据库中的适配性 6
3.4数据挖掘结果的可视化表达 6
3.5数据挖掘技术的实际应用场景 7
4数据库中异常检测与数据挖掘的融合研究 7
4.1异常检测与数据挖掘的关系分析 7
4.2融合技术的设计原则与方法 8
4.3基于挖掘技术的异常模式识别 8
4.4实验验证与案例分析 9
4.5融合技术的未来发展方向 9
结论 10
参考文献 11
致 谢 12
