Hierarchical clustering is an important technique to organize big data for exploratory data analysis. However, existing one-size-fits-all hierarchical clustering methods often fail to meet the diverse needs of different users. To address this challenge, we present an interactive steering method to visually supervise constrained hierarchical clustering by utilizing both public knowledge (e.g., Wikipedia) and private knowledge from users. The novelty of our approach includes 1) automatically constructing constraints for hierarchical clustering using knowledge (knowledge-driven) and intrinsic data distribution (data-driven), and 2) enabling the interactive steering of clustering through a visual interface (user-driven). Our method first maps each data item to the most relevant items in a knowledge base. An initial constraint tree is then extracted using the ant colony optimization algorithm. The algorithm balances the tree width and depth and covers the data items with high confidence. Given the constraint tree, the data items are hierarchically clustered using evolutionary Bayesian rose tree. To clearly convey the hierarchical clustering results, an uncertainty-aware tree visualization has been developed to enable users to quickly locate the most uncertain sub-hierarchies and interactively improve them. The quantitative evaluation and case study demonstrate that the proposed approach facilitates the building of customized clustering trees in an efficient and effective manner.

中文翻译：

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层次聚类的交互式引导


层次聚类是组织大数据以进行探索性数据分析的重要技术。然而，现有的一刀切的层次聚类方法往往无法满足不同用户的多样化需求。为了应对这一挑战，我们提出了一种交互式引导方法，通过利用公共知识（例如维基百科）和用户的私有知识来直观地监督受约束的层次聚类。我们方法的新颖性包括：1）使用知识（知识驱动）和内在数据分布（数据驱动）自动构建层次聚类的约束，2）通过可视化界面（用户驱动）实现聚类的交互式引导。我们的方法首先将每个数据项映射到知识库中最相关的项。然后使用蚁群优化算法提取初始约束树。该算法平衡了树的宽度和深度，并以高置信度覆盖数据项。给定约束树，数据项使用进化贝叶斯玫瑰树进行分层聚类。为了清楚地传达层次聚类结果，开发了不确定性感知树可视化，使用户能够快速定位最不确定的子层次结构并交互式地改进它们。定量评估和案例研究表明，所提出的方法有助于以高效且有效的方式构建定制的聚类树。