摘 要
随着大数据时代的到来，数据挖掘技术在各领域的应用日益广泛，但传统数据挖掘算法在处理海量数据时面临效率低下和可扩展性不足的问题。为此，本研究以优化基于数据库的数据挖掘算法为核心，旨在提升算法性能并拓展其实际应用场景。研究通过分析现有算法的瓶颈问题，提出了一种结合索引结构与并行计算的改进方法，该方法利用数据库管理系统（DBMS）的特性，显著减少了数据扫描次数，并通过多线程技术加速了计算过程。实验结果表明，优化后的算法在处理大规模数据集时，运行时间较传统算法平均减少约40%，同时内存使用量也得到了有效控制。此外，本研究还将优化算法应用于电子商务领域的产品推荐系统中，验证了其在真实场景中的可行性和优越性。研究的主要创新点在于将数据库索引机制与数据挖掘算法深度融合，突破了传统方法对数据存储和访问模式的限制，为解决大规模数据挖掘问题提供了新思路。总体而言，本研究不仅提升了数据挖掘算法的效率，还为其在复杂业务环境中的应用奠定了坚实基础，具有重要的理论价值和实践意义。
关键词：数据挖掘算法优化；数据库索引机制；并行计算；大规模数据处理；产品推荐系统
Abstract
With the advent of the big data era, data mining technologies have been increasingly applied across various fields. However, traditional data mining algorithms face challenges such as low efficiency and insufficient scalability when processing massive datasets. To address these issues, this study focuses on optimizing database-based data mining algorithms to enhance their performance and broaden their practical applications. By analyzing the bottleneck problems of existing algorithms, a novel approach that integrates indexing structures with parallel computing is proposed. This method leverages the characteristics of Database Management Systems (DBMS) to significantly reduce the number of data scans and accelerates computation through multi-threading technology. Experimental results demonstrate that the optimized algorithm reduces runtime by approximately 40% on average compared to traditional algorithms when handling large-scale datasets, while effectively controlling memory usage. Furthermore, the improved algorithm was applied to a product recommendation system in the e-commerce domain, validating its feasibility and superiority in real-world scenarios. The primary innovation of this research lies in the deep integration of database indexing mechanisms with data mining algorithms, which overcomes the limitations of traditional methods regarding data storage and access patterns and provides new insights into solving large-scale data mining problems. Overall, this study not only improves the efficiency of data mining algorithms but also lays a solid foundation for their application in complex business environments, offering significant theoretical value and practical implications.
Keywords: Data Mining Algorithm Optimization; Database Indexing Mechanism; Parallel Computing; Large-Scale Data Processing; Product Recommendation System
目 录
1绪论 1
1.1数据挖掘算法优化的研究背景 1
1.2数据库驱动的数据挖掘意义分析 1
1.3国内外研究现状与发展趋势 1
1.4本文研究方法与技术路线 2
2数据挖掘算法的数据库适配性优化 2
2.1数据库环境下的算法性能瓶颈 2
2.2算法优化的核心技术路径 3
2.3数据存储结构对算法效率的影响 3
2.4并行计算在数据挖掘中的应用 4
2.5数据库适配性优化的案例分析 4
3数据挖掘算法的效率提升策略 5
3.1高效索引机制的设计与实现 5
3.2数据预处理对算法效率的作用 5
3.3分布式架构下的算法优化实践 6
3.4算法复杂度的评估与改进方法 6
3.5效率提升的实际应用场景 7
4数据挖掘算法优化的应用探索 7
4.1商业智能中的算法优化实践 7
4.2医疗数据分析的算法适配研究 8
4.3金融风控中的高效算法应用 8
4.4社交网络分析的优化策略探讨 9
4.5跨领域应用的综合效果评估 9
结论 10
参考文献 11
致 谢 12
