Fruchterman-Reingold 图可视化与凝聚聚类之间的关联
Interrelating Fruchterman-Reingold Graph Visualization and Agglomerative Clustering
- University of São Paulo（圣保罗大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文探讨Fruchterman-Reingold图可视化与四种凝聚聚类方法间的关联，发现两者关系在不同数据上稳定，且可视化与原始数据相似度较高，但与聚类结果相似度较低。
中文摘要 AI 辅助
图可视化方法和凝聚聚类在数据分析和模式识别中经常被考虑。由于这些方法相互关联且互补，研究它们之间的关联尤为引人关注。在本工作中，我们研究了 Fruchterman-Reingold 图可视化方法与采用单连接、全连接、平均和 Ward 连接准则的四种凝聚聚类之间可能的关系。我们考虑了三种类型的数据集，分别在 2 维和 10 维空间中，以及后者到二维的 PCA 投影。获得的结果表明，所考虑的方法之间的关系在上述三种类型的数据上变化不大。同时，凝聚方法倾向于产生彼此大多相似的结果，而与原始数据呈现中等程度的相似性。Fruchterman-Reingold 可视化结果与原始数据相似，但与凝聚方法相比表现出相对较小的相似性。
英文摘要
Graph visualization methods and agglomerative clustering have been frequently considered in data analysis and pattern recognition. Because these approaches are interrelated and complementary, it is of particular interest to investigate their associations. In this work, we study the possible relationship between the Fruchterman-Reingold graph visualization method and four types of agglomerative clustering adopting single- and complete-linkage, average, and Ward's linkage criteria. Three types of datasets have been considered in 2 and 10 dimensions, as well as the PCA projection of the latter to two dimensions. The results obtained suggest that the relationship between the methods considered did not vary much for the three types of data mentioned above. At the same time, the agglomerative methods tended to yield results that are mostly similar to each other, while presenting moderate similarity with the original data. The Fruchterman-Reingold visualization resulted similar to the original data, but exhibited relatively smaller similarity to the agglomerative methods.
