提出新方法TNStream，高效处理多密度流数据聚类。
TNStream: Applying Tightest Neighbors to Micro-Clusters to Define Multi-Density Clusters in Streaming Data
- 用紧邻点定义局部相似性，自适应调整聚类半径。
- 在多密度数据上显著提升聚类质量，优于现有方法。
- 适合高维、复杂密度变化的实时数据聚类任务。
在数据流聚类中，系统性理论仍较匮乏。近年来，基于密度的方法受到关注，但现有算法难以同时应对任意形状、多密度、高维数据，并保持强抗离群能力。当数据密度复杂变化时，聚类质量显著下降。本文提出基于紧邻点（Tightest Neighbors）的新概念，构建骨架集（Skeleton Set）为基础的数据流聚类理论，并据此开发全在线算法TNStream。该算法根据局部相似性自适应确定聚类半径，通过微簇总结多密度数据流演化过程，并利用紧邻点策略生成最终聚类。为提升高维场景效率，引入局部敏感哈希（LSH）结构化微簇，解决存储k近邻的挑战。在多种合成与真实数据集上，使用不同聚类指标评估，实验表明其有效提升多密度数据的聚类质量，验证了所提理论的有效性。
原文摘要 · Abstract (English)
In data stream clustering, systematic theory of stream clustering algorithms remains relatively scarce. Recently, density-based methods have gained attention. However, existing algorithms struggle to simultaneously handle arbitrarily shaped, multi-density, high-dimensional data while maintaining strong outlier resistance. Clustering quality significantly deteriorates when data density varies complexly. This paper proposes a clustering algorithm based on the novel concept of Tightest Neighbors and introduces a data stream clustering theory based on the Skeleton Set. Based on these theories, this paper develops a new method, TNStream, a fully online algorithm. The algorithm adaptively determines the clustering radius based on local similarity, summarizing the evolution of multi-density data streams in micro-clusters. It then applies a Tightest Neighbors-based clustering algorithm to form final clusters. To improve efficiency in high-dimensional cases, Locality-Sensitive Hashing (LSH) is employed to structure micro-clusters, addressing the challenge of storing k-nearest neighbors. TNStream is evaluated on various synthetic and real-world datasets using different clustering metrics. Experimental results demonstrate its effectiveness in improving clustering quality for multi-density data and validate the proposed data stream clustering theory.
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