摘 要
关联规则挖掘技术作为数据挖掘领域的重要组成部分，在数据库分析中具有广泛的应用价值。随着大数据时代的到来，海量数据的存储与管理为关联规则挖掘提供了丰富的研究素材，同时也带来了新的挑战。本研究旨在探讨数据库中关联规则挖掘的核心技术及其实际应用，通过改进传统算法以适应复杂数据环境的需求。研究基于Apriori算法和FP-Growth算法等经典方法，提出了一种结合数据压缩与并行处理的优化策略，显著提升了规则挖掘的效率与准确性。实验结果表明，该方法在大规模数据库中的性能表现优于现有算法，特别是在稀疏数据集上的挖掘能力得到了明显增强。此外，本研究还将优化后的关联规则挖掘技术应用于市场篮子分析、医疗诊断辅助以及用户行为预测等多个领域，验证了其跨领域的适用性。研究的主要贡献在于提出了针对高维稀疏数据的高效挖掘方案，并通过实际案例展示了其在解决现实问题中的潜力，为未来相关技术的发展提供了理论支持与实践参考。
关键词：关联规则挖掘；高维稀疏数据；Apriori算法；FP-Growth算法；数据压缩与并行处理
Abstract
Association rule mining, as a critical component of data mining, plays an essential role in database analysis with extensive application value. In the era of big data, the storage and management of massive datasets provide rich research materials for association rule mining while also presenting new challenges. This study focuses on exploring the core technologies of association rule mining in databases and their practical applications, aiming to enhance traditional algorithms to meet the demands of complex data environments. Based on classical methods such as the Apriori algorithm and FP-Growth algorithm, this research proposes an optimized strategy that integrates data compression with parallel processing, significantly improving the efficiency and accuracy of rule mining. Experimental results demonstrate that this approach outperforms existing algorithms in large-scale databases, particularly enhancing mining capabilities in sparse datasets. Furthermore, the optimized association rule mining technology has been applied to multiple domains, including market basket analysis, medical diagnostic assistance, and user behavior prediction, validating its cross-domain applicability. The primary contribution of this study lies in proposing an efficient mining solution for high-dimensional sparse data and showcasing its potential in addressing real-world problems through practical case studies, thereby providing theoretical support and practical references for the future development of related technologies.
Keywords: Association Rule Mining; High-Dimensional Sparse Data; Apriori Algorithm; Fp-Growth Algorithm; Data Compression And Parallel Processing
目 录
1绪论 1
1.1数据库中关联规则挖掘的研究背景 1
1.2关联规则挖掘技术的意义与价值 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2关联规则挖掘技术基础 2
2.1数据库与数据挖掘的基本概念 2
2.2关联规则挖掘的核心原理 3
2.3常见的关联规则挖掘算法综述 4
2.4算法性能评估指标体系 4
2.5技术实现中的关键问题 5
3关联规则挖掘技术的应用场景分析 5
3.1商业领域中的应用需求 5
3.2医疗健康领域的实践案例 6
3.3零售行业中的推荐系统设计 6
3.4金融风控中的规则挖掘应用 7
3.5教育数据分析中的潜在价值 7
4关联规则挖掘技术的优化与挑战 7
4.1大规模数据下的效率提升策略 8
4.2高维稀疏数据的处理方法 8
4.3挖掘结果的可解释性改进 8
4.4安全与隐私保护的技术手段 9
4.5未来发展方向与潜在突破 10
结论 10
参考文献 12
致 谢 13
