科研之友
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摘要
Classical clustering algorithms use all features to partition a dataset, making it difficult for users to understand the clustering results. Some scholars have proposed interpretable clustering algorithms that use a few understandable features to explain clustering results. However, the existing algorithms can only generate one interpretation and fail to satisfy the diverse needs of different users. To address this challenge, the Clustering Customized Explanation Tree (CCET), a visual analytics system, was constructed in this paper. The system helps users modify existing explanations to obtain customized explanations. Firstly, a variety of views are designed to visualize the explanations and help users judge whether the existing explanations meet the requirements. Then, an explanations modification strategy based on cluster centroids splitting is proposed making it easy for users to revise explanations according to the requirement. We demonstrate the CCET using a case study and a user study. The results show that the system can deepen users' understanding of clustering results and make it easy for them to conduct further decision analysis.
