用稀疏图结构实现高效可扩展的主动聚类，大幅减少人工标注需求。
Data Skeleton Learning: Scalable Active Clustering with Sparse Graph Structures
- 构建数据骨架图与更新图协同优化聚类
- 仅需少量用户约束即可实现高精度聚类
- 适合大规模数据场景下的智能标注与聚类
本文聚焦于基于成对约束的主动聚类在大数据应用中的效率与可扩展性问题，涵盖数据挖掘、知识标注及AI模型预训练等场景。目标有三：降低迭代聚类更新的计算开销；增强用户约束的影响，减少标注需求以实现精准聚类；降低实际部署中的内存占用。为此，提出一种基于图结构的主动聚类算法，利用两个稀疏图——一个用于表示数据间关系（所提数据骨架），另一个用于更新该骨架。二者协同工作，通过细化数据骨架中的连通子图，生成嵌套聚类。实证分析表明，该算法在保持多种距离度量鲁棒性的前提下，显著减少用户输入约束，同时在计算性能和可扩展性上优于现有方法。
原文摘要 · Abstract (English)
In this work, we focus on the efficiency and scalability of pairwise constraint-based active clustering, crucial for processing large-scale data in applications such as data mining, knowledge annotation, and AI model pre-training. Our goals are threefold: (1) to reduce computational costs for iterative clustering updates; (2) to enhance the impact of user-provided constraints to minimize annotation requirements for precise clustering; and (3) to cut down memory usage in practical deployments. To achieve these aims, we propose a graph-based active clustering algorithm that utilizes two sparse graphs: one for representing relationships between data (our proposed data skeleton) and another for updating this data skeleton. These two graphs work in concert, enabling the refinement of connected subgraphs within the data skeleton to create nested clusters. Our empirical analysis confirms that the proposed algorithm consistently facilitates more accurate clustering with dramatically less input of user-provided constraints, and outperforms its counterparts in terms of computational performance and scalability, while maintaining robustness across various distance metrics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。