摘 要
随着大数据时代的到来，数据规模呈指数级增长，传统聚类分析算法在处理海量数据时面临计算效率低下和可扩展性不足的问题，难以满足现代数据库系统对高效数据分析的需求。为此，本研究旨在探索面向大数据的聚类分析算法在数据库中的应用，提出一种基于分布式计算框架的改进聚类算法，以提升其在大规模数据集上的性能表现。该算法通过优化数据划分策略、引入近似计算方法以及结合索引技术，显著降低了计算复杂度，并增强了算法对噪声数据的鲁棒性。实验结果表明，相较于传统聚类算法，所提方法在处理TB级数据时能够实现更高的聚类精度和更快的收敛速度，同时具备良好的可扩展性。此外，本研究还将该算法应用于实际数据库场景，验证了其在用户行为分析、异常检测等任务中的有效性。主要创新点在于将分布式计算与聚类分析深度融合，设计了一种适应大数据环境的高效算法，并通过理论分析与实证研究证明了其优越性。研究成果为大数据背景下数据库系统的智能化分析提供了新思路，具有重要的理论意义和应用价值。
关键词：大数据聚类；分布式计算；算法优化；数据划分策略；噪声鲁棒性
Abstract
With the advent of the big data era, the scale of data is growing exponentially. Traditional clustering analysis algorithms face challenges of low computational efficiency and insufficient scalability when dealing with massive datasets, making it difficult to meet the demands of modern database systems for efficient data analysis. To address this issue, this study explores the application of clustering analysis algorithms tailored for big data in databases and proposes an improved clustering algorithm based on a distributed computing fr amework to enhance its performance on large-scale datasets. By optimizing data partitioning strategies, incorporating approximate computation methods, and integrating indexing techniques, the algorithm significantly reduces computational complexity while improving robustness against noisy data. Experimental results demonstrate that, compared with traditional clustering algorithms, the proposed method achieves higher clustering accuracy and faster convergence rates when processing data at the TB level, along with excellent scalability. Furthermore, this study applies the algorithm to real-world database scenarios, validating its effectiveness in tasks such as user behavior analysis and anomaly detection. The primary innovation lies in the deep integration of distributed computing and clustering analysis, resulting in the design of an efficient algorithm adapted to the big data environment. Both theoretical analysis and empirical research confirm its superiority. This research provides new insights into intelligent analysis of database systems under the context of big data, offering significant theoretical implications and practical value.
Keywords: Big Data Clustering; Distributed Computing; Algorithm Optimization; Data Partitioning Strategy; Noise Robustness
目 录
1绪论 1
1.1面向大数据聚类分析的研究背景 1
1.2数据库中聚类算法应用的意义 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大数据环境下的聚类算法基础 2
2.1聚类分析的基本概念与分类 2
2.2常见聚类算法的原理与特点 3
2.3大数据对传统聚类算法的挑战 3
2.4面向大数据的改进聚类算法综述 4
2.5聚类算法在数据库中的适配性分析 4
3聚类算法在数据库中的实现与优化 5
3.1数据库系统对聚类算法的需求分析 5
3.2高效数据预处理方法的设计 5
3.3面向大规模数据的分布式聚类算法 6
3.4聚类算法性能优化策略研究 6
3.5实验验证与结果分析 7
4聚类分析在数据库应用中的案例研究 7
4.1数据挖掘场景中的聚类应用实例 7
4.2用户行为分析中的聚类算法实践 8
4.3数据库异常检测中的聚类技术应用 8
4.4聚类算法在推荐系统中的优化方案 9
4.5案例总结与经验提炼 9
结论 10
参考文献 11
致 谢 12
