arXiv：cs.LG· Justin Lin, Julia Fukuyama·· 4 小时前AI 评分31
DRTool：分析高维聚类结果的交互式工具
DRtool: An Interactive Tool for Analyzing High-Dimensional Clusterings
AI 导读
研究者开发了 R 包 DRTool，提供可视化评估与假设检验两类新的聚类验证技术，帮助分析人员识别高维数据中的虚假聚类并更好解读聚类结果。DRTool 以交互式工具箱形式提供，论文共 35 页、含 14 张图。
正文
Abstract:When faced with new data, we often conduct a cluster analysis to obtain a better understanding of the data's structure and the archetypical samples present in the data. However, the increases in data complexity and dimensionality have made this step very tricky. The large proportion of noise in high-dimensional data blurs patterns and trends, making clusters difficult to distinguish. As such, cluster-discovery tools and cluster-verification tools must be adapted to address the difficulties of high-dimensional data. Nonlinear dimension reduction is a step in the right direction, but even these methods are known to produce false structures, especially when mishandled. A common phenomenon that often goes undetected by the untrained eye is over-clustering of the data. In continuation of these efforts, we developed new cluster verification techniques, including visual assessments and a hypothesis test, that help analysts distinguish false clusters and better interpret their high-dimensional clustering results. For ease of use, these new methods are provided in an interactive toolbox available via R package DRTool.
| Comments: | 35 pages, 14 figures |
| Subjects: | Applications (stat.AP); Machine Learning (cs.LG) |
| Cite as: | arXiv:2509.04603 [stat.AP] |
| (or arXiv:2509.04603v4 [stat.AP] for this version) | |
| https://doi.org/10.48550/arXiv.2509.04603 arXiv-issued DOI via DataCite |
Submission history
From: Justin Lin [view email]
[v1]
Thu, 4 Sep 2025 18:36:28 UTC (649 KB)
[v2]
Thu, 11 Sep 2025 18:37:27 UTC (649 KB)
[v3]
Fri, 3 Apr 2026 15:27:41 UTC (2,092 KB)
[v4]
Mon, 5 Oct 2026 19:13:14 UTC (2,095 KB)
来源：arXiv：cs.LG · arxiv.org