MOStream：一种模块化且自优化的数据流聚类算法
MOStream: A Modular and Self-Optimizing Data Stream Clustering Algorithm
AI总结：
本文提出模块化自优化数据流聚类算法 MOStream，通过分离汇总结构、窗口模型、离群点检测和簇精炼四个维度，在真实与合成数据集上显著提升准确性、吞吐量和适应性。
摘要：
数据流聚类是从物联网（IoT）到社交媒体和金融系统等各类真实应用中的一项关键操作。现有数据流聚类算法虽然在不同程度上有效，但往往缺乏适应多样化工作负载特征（例如离群点、簇演化以及数据点维度变化）所需的灵活性和自优化能力。这些局限表现为聚类准确性欠佳和计算效率低下。本文提出 MOStream，这是一种模块化且自优化的数据流聚类算法，旨在运行时动态平衡聚类准确性与计算效率。MOStream 的突出之处在于其适应性，明确划分了四个关键设计维度：用于汇总的数据结构、处理数据时间性的窗口模型、离群点检测机制，以及用于提升簇质量的精炼策略。这种清晰的分离促进了对不同设计选择的灵活适配，并增强了其对广泛应用场景的适应性。我们对 MOStream 进行了严格的性能评估，采用多种配置，并在 4 个真实数据集和 3 个合成数据集上将其与 9 种具有代表性的数据流聚类算法进行基准比较。实验结果表明，MOStream 在聚类准确性、处理吞吐量以及对不同数据流特征的适应性方面始终优于竞争算法。
英文摘要：
Data stream clustering is a critical operation in various real-world applications, ranging from the Internet of Things (IoT) to social media and financial systems. Existing data stream clustering algorithms, while effective to varying extents, often lack the flexibility and self-optimization capabilities needed to adapt to diverse workload characteristics such as outlier, cluster evolution and changing dimensions in data points. These limitations manifest in suboptimal clustering accuracy and computational inefficiency. In this paper, we introduce MOStream, a modular and self-optimizing data stream clustering algorithm designed to dynamically balance clustering accuracy and computational efficiency at runtime. MOStream distinguishes itself by its adaptivity, clearly demarcating four pivotal design dimensions: the summarizing data structure, the window model for handling data temporality, the outlier detection mechanism, and the refinement strategy for improving cluster quality. This clear separation facilitates flexible adaptation to varying design choices and enhances its adaptability to a wide array of application contexts. We conduct a rigorous performance evaluation of MOStream, employing diverse configurations and benchmarking it against 9 representative data stream clustering algorithms on 4 real-world datasets and 3 synthetic datasets. Our empirical results demonstrate that MOStream consistently surpasses competing algorithms in terms of clustering accuracy, processing throughput, and adaptability to varying data stream characteristics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
