Interactive Steering of Hierarchical Clustering
Weikai Yang, Xiting Wang, Jie Lu, Wenwen Dou, Shixia Liu

TL;DR
This paper introduces an interactive method for hierarchical clustering that incorporates user and knowledge-based constraints, allowing for customizable and visually guided data organization.
Contribution
It presents a novel approach combining automatic constraint construction with interactive visual steering for hierarchical clustering.
Findings
Facilitates customized hierarchical clustering with high efficiency.
Enables users to interactively refine clustering results.
Demonstrates effectiveness through quantitative evaluation and case studies.
Abstract
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…
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