Financial cluster analysis allows investors to discover investment alternatives and avoid undertaking excessive risks. However, this analytical task faces substantial challenges arising from many pairwise comparisons, the dynamic correlations across time spans, and the ambiguity in deriving implications from business relational knowledge. We propose Prismatic, a visual analytics system that integrates quantitative analysis of historical performance and qualitative analysis of business relational knowledge to cluster correlated businesses interactively. Prismatic features three clustering processes: dynamic cluster generation, knowledge-based cluster exploration, and correlation-based cluster validation. Utilizing a multi-view clustering approach, it enriches data-driven clusters with knowledge-driven similarity, providing a nuanced understanding of business correlations. Through well-coordinated visual views, Prismatic facilitates a comprehensive interpretation of intertwined quantitative and qualitative features, demonstrating its usefulness and effectiveness via case studies on formulating concept stocks and extensive interviews with domain experts.

基于历史绩效的定量分析和基于商业关系知识的定性分析的可视化分析系统Prismatic，通过动态聚类生成、基于知识的聚类探索和基于相关性的聚类验证，丰富了数据驱动聚类的知识驱动相似性，从而提供了对商业关联性的细致理解，通过对概念股的制定和与领域专家的广泛访谈的案例研究，证明了该系统的实用性和效果。

棱镜：概念股的交互式多视角聚类分析